Therapy control based on a patient movement state

ABSTRACT

A movement state of a patient is detected based on brain signals, such as an electroencephalogram (EEG) signal. In some examples, a brain signal within a dorsal-lateral prefrontal cortex of a brain of the patient indicative of prospective movement of the patient may be sensed in order to detect the movement state. The movement state may include the brain state that indicates the patient is intending on initiating movement, initiating movement, attempting to initiate movement or is actually moving. In some examples, upon detecting the movement state, a movement disorder therapy is delivered to the patient. In some examples, the therapy delivery is deactivated upon detecting the patient is no longer in a movement state or that the patient has successfully initiated movement. In addition, in some examples, the movement state detected based on the brain signals may be confirmed based on a signal from a motion sensor.

This application is a divisional of U.S. application Ser. No.12/237,799, filed Sep. 25, 2008, which issued as U.S. Pat. No. 8,121,694on Feb. 21, 2012. U.S. application Ser. No. 12/237,799 claims thebenefit of U.S. Provisional Application No. 60/999,096 by Molnar et al.,entitled, “DEVICE CONTROL BASED ON PROSPECTIVE MOVEMENT” and filed onOct. 16, 2007 and U.S. Provisional Application No. 60/999,097 by Denisonet al., entitled, “RESPONSIVE THERAPY SYSTEM” and filed on Oct. 16,2007. The entire content of each of the identified U.S. Applications isincorporated herein by reference.

TECHNICAL FIELD

The disclosure relates to therapy systems, and, more particularly,controlling a therapy system.

BACKGROUND

Patients afflicted with movement disorders or other neurodegenerativeimpairment, whether by disease or trauma, may experience muscle controland movement problems, such as rigidity, bradykinesia (i.e., slowphysical movement), rhythmic hyperkinesia (e.g., tremor), nonrhythmichyperkinesia (e.g., tics) or akinesia (i.e., a loss of physicalmovement). Movement disorders may be found in patients with Parkinson'sdisease, multiple sclerosis, and cerebral palsy, among other conditions.Delivery of electrical stimulation and/or a fluid (e.g., apharmaceutical drug) to one or more sites in a patient, such as a brain,spinal cord, leg muscle or arm muscle, in a patient may help alleviate,and in some cases, eliminate symptoms associated with movementdisorders.

In some cases, delivery of an external cue, such as a visual, auditoryor somatosensory cue, to the patient may also help control someconditions of a movement disorder. For example, delivery of an externalcue to the patient may help a patient susceptible to gait freeze orakinesia to initiate movement.

SUMMARY

In general, the disclosure is directed toward controlling therapydelivery to a patient based on a determination of whether a patient isin a movement state based on a brain signal of the patient. For example,some systems and techniques in accordance with this disclosure maydetermine whether a patient is in a rest (i.e., non-movement) state or amovement state based on a brain signal and control a device to delivertherapy to the patient or change therapy parameter values upondetermining the patient is in the movement state. The movement stateincludes the state in which the patient is generating thoughts ofmovement (i.e., is intending to move), attempting to initiate movementor is actually undergoing movement.

In some examples, a device may be controlled based on detection of abrain signal within a dorsal-lateral prefrontal (DLPF) cortex of apatient that is indicative of prospective movement of the patient. Thedevice may include, for example, a non-medical appliance (e.g., a lamp),a patient transport device (e.g., a wheelchair or a prosthetic limb) ora therapy delivery device.

In one aspect, the disclosure is directed to a method that includesmonitoring a bioelectrical signal from a brain of a patient, determiningwhether the bioelectrical brain signal indicates the patient is in amovement state, at a first time, controlling delivery of therapy to thepatient if the bioelectrical signal indicates the patient is in themovement state, at a second time following the first time, determiningwhether the patient is in the movement state, and controlling thedelivery of the therapy to the patient based on whether the patient isin the movement state at the second time following the first time. Forexample, the method may include at a second time following the firsttime, confirming that the patient is in the movement state based on asignal other than the brain signal.

In another aspect, the disclosure is directed to a system comprising asensing module to monitor a bioelectrical brain signal of a patient anda processor that determines whether the bioelectrical brain signalindicates the patient is in a movement state and, at a first time,controls delivery of therapy to the patient if the bioelectrical brainsignal indicates the patient is in a movement state. The processor, at asecond time following the first time, determines whether the patient isin the movement state and controls the delivery of the therapy to thepatient based on whether the patient is in the movement state at thesecond time following the first time.

In another aspect, the disclosure is directed to a computer-readablemedium comprising instructions. The instructions cause a programmableprocessor to receive a bioelectrical brain signal, determine whether thebioelectrical brain signal indicates the patient is in a movement state,at a first time, control operation of a therapy device if thebioelectrical brain signal indicates the patient is in a movement state,at a second time following the first time, determine whether the patientis in the movement state, and control the operation of the therapydevice based on whether the patient is in the movement state at thesecond time following the first time.

In another aspect, the disclosure is directed to a method comprisingmonitoring an EEG signal from a brain of a patient, determining whetherthe EEG signal indicates the patient is in a movement state, controllingdelivery of a sensory cue to the patient if the EEG signal indicates thepatient is in the movement state, and confirm the patient is in themovement state based on a motion sensor.

In another aspect, the disclosure is directed to a method comprisingmeans for monitoring a bioelectrical brain signal from a brain of apatient, means for determining whether the brain signal indicates thepatient is in a movement state, means for controlling delivery oftherapy to the patient if the brain signal indicates the patient is inthe movement state at a first time; means for determining whether thepatient is in the movement state at a second time following the firsttime, and means for controlling the delivery of the therapy to thepatient based on whether the patient is in the movement state at thesecond time following the first time.

In another aspect, the disclosure is directed to a method comprisingsensing a brain signal indicative of prospective movement of a patientwithin a dorsal-lateral prefrontal cortex of a brain of the patient, andcontrolling delivery of movement disorder therapy to the patient basedon the sensed brain signal.

In another aspect, the disclosure is directed to a system comprising asensing module to sense a brain signal indicative of prospectivemovement of a patient within a dorsal-lateral prefrontal cortex of abrain of the patient, and a controller to control delivery of movementdisorder therapy to the patient based on the sensed brain signal.

In another aspect, the disclosure is directed to a method comprisingsensing a brain signal indicative of prospective movement of a patientwithin a dorsal-lateral prefrontal cortex of a brain of the patient, andcontrolling operation of a device based on the sensed brain signal.

In another aspect, the disclosure is directed to a system comprising asensing module to sense a brain signal indicative of prospectivemovement of a patient within a dorsal-lateral prefrontal cortex of abrain of the patient, and a controller that controls a device based onthe sensed brain signal.

In another aspect, the disclosure is directed to a system comprisingmeans for sensing a brain signal indicative of prospective movement of apatient within a dorsal-lateral prefrontal cortex of a brain of thepatient, and means for controlling operation of a device based on thesensed brain signal.

In another aspect, the disclosure is directed to a computer-readablemedium containing instructions. The instructions cause a programmableprocessor to receive input indicating a signal from a dorsal-lateralprefrontal cortex of a brain of a patient, determine whether the signalindicates prospective movement of the patient, and control operation ofa device if the signal indicates prospective movement.

In other aspects, the disclosure is directed toward a computer-readablemedium containing instructions. The instructions cause a programmableprocessor to perform any part of the techniques described herein.

The details of one or more examples of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the disclosure will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is a schematic diagram illustrating an example therapy systemthat delivers therapy to control a movement disorder of a patient.

FIG. 1B is a top view of the head of the patient shown in FIG. 1A andillustrates an example electrode array.

FIG. 2 is a schematic diagram of another example therapy system, whichincludes an external cue device, an implanted medical device, and aprogrammer.

FIG. 3 is a block diagram illustrating an example sensing device.

FIG. 4 is a block diagram illustrating various components of an exampleexternal cue device.

FIG. 5 is a schematic diagram of another example therapy system, whichincludes an external sensing device and an implanted therapy deliverydevice.

FIG. 6 is a block diagram illustrating various components of theimplanted therapy delivery device of FIG. 5.

FIG. 7 is a flow diagram of an example technique for controlling atherapy device based on an electroencephalogram (EEG) signal.

FIG. 8 is a flow diagram of an example technique for controlling atherapy device based on one or more frequency characteristics of an EEGsignal.

FIG. 9 is a flow diagram of another example of the technique shown inFIG. 8.

FIG. 10 is a flow diagram of an example technique for titrating therapybased on the strength of an EEG signal within a particular frequencyband.

FIG. 11 is a flow diagram of an example technique for deactivating oradjusting therapy delivery in response to detecting a cessation ofmovement or a successfully initiation of movement.

FIG. 12 is a flow diagram of an example technique for controlling atherapy device based on an EEG signal and a signal from a motion sensor.

FIG. 13 is a schematic diagram illustrating example motion sensors thatmay be used to monitor an activity level of a patient to detect amovement state of a patient.

FIG. 14 is a flow diagram of an example technique for determining theEEG signal characteristic that indicates a patient is in a movementstate.

FIG. 15 is a block diagram illustrating an example therapy system fortreating movement disorders and illustrates various components of amedical device.

FIG. 16 illustrates a therapy system in which activity sensed within adorsal lateral prefrontal cortex (DLPF) is used to control an externaldevice.

FIG. 17 is a flow diagram of an example technique for controlling adevice, such as an external device or therapy delivery device, based ona brain signal within the DLPF cortex of a patient.

FIG. 18A is a flow diagram illustrating an example technique foranalyzing electrical activity within the DLPF cortex to determinewhether the activity indicates prospective patient movement.

FIG. 18B is a flow diagram illustrating a technique for determining oneor more threshold amplitude values for determining whether electricalactivity within the DLPF cortex is indicative of prospective movement.

FIG. 19A is a flow diagram illustrating an example technique foranalyzing electrical activity within the DLPF cortex to determinewhether the activity indicates prospective patient movement.

FIG. 19B is a flow diagram illustrating an example technique fordetermining one or more trend templates to compare to a pattern ofamplitude measurements of electrical activity within the DLPF cortex inorder to determining whether the electrical activity is indicative ofprospective movement.

FIG. 20 is a conceptual frequency domain electroencephalogram plot takenby a sensor positioned near an occipital cortex of a human subject, anddemonstrates that tuning to a particular frequency band may reveal moreuseful information about certain brain activity.

FIG. 21 is flow diagram of an example technique for controlling therapydelivery based on one or more frequency characteristics of a brainsignal within a DLPF cortex of a patient.

FIG. 22 is a block diagram illustrating an example frequency selectivesignal monitor that includes a chopper-stabilized superheterodyneamplifier and a signal analysis unit.

FIG. 23 is a block diagram illustrating a portion of an examplechopper-stabilized superheterodyne amplifier that may be used within thefrequency selective signal monitor from FIG. 22.

FIGS. 24A-24D are graphs illustrating the frequency components of asignal at various stages within the superheterodyne amplifier of FIG.23.

FIG. 25 is a block diagram illustrating a portion of an examplechopper-stabilized superheterodyne amplifier with in-phase andquadrature signal paths for use within a frequency selective signalmonitor.

FIG. 26 is a circuit diagram illustrating an example chopper-stabilizedmixer amplifier that may be used within the frequency selective signalmonitor of FIG. 22.

FIG. 27 is a circuit diagram illustrating an example chopper-stabilized,superheterodyne instrumentation amplifier with differential inputs.

DETAILED DESCRIPTION

Therapy delivery to a patient may be controlled based on a determinationof whether a patient is in a movement state based on a brain signal ofthe patient. The brain signal may include a bioelectrical signal, suchas an electroencephalogram (EEG) signal, an electrocorticogram (ECoG)signal, a signal generated from measured field potentials within one ormore regions of a patient's brain and/or action potentials from singlecells within the patient's brain. In some examples, the brain signal maybe detected within a dorsal-lateral prefrontal (DLPF) cortex of thepatient's brain. The movement state includes the state in which thepatient is generating thoughts of movement (i.e., is intending to move),initiating movement, attempting to initiate movement or is actuallyundergoing movement. The therapy may include, for example, electricalstimulation, fluid delivery or a sensory cue (e.g., visual,somatosensory or auditory cue) delivered to the patient via an externalor implanted device. The therapy delivery may help the patient controlsymptoms of a movement disorder or other neurodegenerative impairment.For example, in one example, delivery of an external sensory cue mayhelp the patient initiate movement or more effectively undertake orcontinue movement.

In order to determine whether the bioelectrical signal indicates thepatient is in a movement state or a rest state, the bioelectrical signalmay be analyzed for comparison of a voltage or amplitude value with astored value, temporal or frequency correlation with a template signal,a particular power level within a particular frequency band of thebioelectrical signal, or combinations thereof. In one example, aprocessor of a bioelectrical sensing device may monitor the power levelof the mu rhythm within an alpha frequency band (e.g., about 5 Hertz(Hz) to about 10 Hz) of an EEG signal. If the power level of the murhythm falls below a particular threshold, which may be determinedduring a trial period, the EEG signal may indicate the patient is in amovement state. The sensing device may then control a therapy device todeliver a therapy to the patient to mitigate the effects of a movementdisorder. For example, the sensing device may generate a control signalthat is transmitted to the therapy device and causes the therapy deviceto initiate therapy delivery or adjust one or more therapy deliveryparameter values.

In some examples, the therapy systems and methods also includedeactivating the delivery of therapy or changing therapy parameters upondetermining the patient is in the rest state (i.e., as stopped moving)or has successfully initiated movement, depending upon the type ofmovement disorder symptom the therapy system is implemented to address.In addition, in some examples, a first determination that the patient isin a movement stated based on brain signals may be confirmed by a seconddetermination that is based on another source that is independent of thebrain signals, such as a motion sensor.

FIG. 1A is a schematic diagram illustrating an example therapy system 10that delivers therapy to control a movement disorder or aneurodegenerative impairment of patient 12. Patient 12 ordinarily willbe a human patient. In some cases, however, the systems and techniquesdescribed herein may be applied to non-human patients. The movementdisorder or other neurodegenerative impairment may include, for example,muscle control, motion impairment or other movement problems, such asrigidity, bradykinesia, rhythmic hyperkinesia, nonrhythmic hyperkinesia,akinesia. In some cases, the movement disorder may be a symptom ofParkinson's disease. However, the movement disorder may be attributableto other patient conditions. Although movement disorders are primarilyreferred to throughout the remainder of the application, the therapysystems and methods described herein are also useful for controllingsymptoms of other conditions, such as neurodegenerative impairment.

Therapy system 10, which includes sensing device 14 and external cuedevice 16, may improve the performance of motor tasks by patient 12 thatmay otherwise be difficult. These tasks include at least one ofinitiating movement, maintaining movement, grasping and moving objects,improving gait associated with narrow turns, and so forth. External cuedevice 16 generates and delivers a sensory cue, such as a visual,auditory or somatosensory cue, to patient 12 in order to help control atleast one symptom of a movement disorder. For example, if patient 12 isprone to gait freeze or akinesia, a sensory cue may help patient 12initiate or maintain movement. In other examples, external cuesdelivered by external cue device 16 may be useful for controlling othermovement disorder conditions, such as, but not limited to, rigidity,bradykinesia, rhythmic hyperkinesia, and nonrhythmic hyperkinesia.

Rather than requiring patient 12 to manually activate external cuedevice 16, therapy system 10 automatically activates external cue device16 in response to a sensed state, condition or event. In some cases,therapy system 10 also automatically deactivates external cue device 16upon determining that active therapy delivery is no longer desirable,e.g., upon determining patient 12 is no longer in a movement state orhas successfully initiated movement. Sensing device 14 detects amovement state of patient 12 based on a brain signal of brain 20 ofpatient 12 and transmits a signal to external cue device 16 in responseto detecting the movement state. The brain signal may be a bioelectricalsignal within one or more regions of brain 20 that indicate patient 12is intending on initiating movement, attempting to initiate movement, oris actually moving. Accordingly, the “movement state” generallyindicates a brain state in which patient 12 is intending on initiatingmovement, attempting to initiate movement (e.g., patient 12 isattempting to move, but because of the movement disorder, patient 12cannot successfully initiate the movement) or is actually moving. Thus,detecting a movement state includes detecting a patient's intention tomove. In contrast, a “rest state” generally indicates a brain state inwhich patient 12 is at rest, i.e., is not intending on moving and is notactually moving.

Examples of bioelectrical signals include an electroencephalogram (EEG)signal, an electrocorticogram (ECoG) signal, a signal generated frommeasured field potentials within one or more regions of brain 20 oraction potentials from single cells within brain 20 (referred to as“spikes”). Determining action potentials of single cells within brain 20may require resolution of bioelectrical signals to the cellular leveland provides fidelity for fine movements, i.e., a bioelectrical signalindicative of fine movements (e.g., slight movement of a finger). Whilethe remainder of the disclosure primarily refers to EEG signals, inother examples, sensing device 14 may be configured to determine whetherpatient 12 is in a movement state based on other types of bioelectricalsignals from within brain 20 of patient 12.

After sensing device 14 determines that patient 12 is in a movementstate, external cue device 16 may deliver a sensory cue, such as avisual, somatosensory or auditory cue, to patient 12 in order to helpcontrol the movement disorder. Automatic activation of external cuedevice 16 may help provide patient 12 with better control and timing oftherapy delivery by external cue device 16 by eliminating the need forpatient 12, who exhibits some difficulty with movement, to manuallyactivate external cue device 16. In addition, automatically initiatingthe delivery of a sensory cue in response to detecting a movement statemay enable therapy system 10 to minimize the time between when patient12 needs the therapy and when the therapy is actually delivered.

Therapy system 10 provides a responsive system for controlling thedelivery of therapy to patient 12. As one example of the responsivenessof therapy system 10, therapy system 10 times the delivery of therapy topatient 12 such that patient 12 receives the therapy at a relevant time,i.e., when it is particularly useful to patient 12. In contrast, anexternal cue device that requires patient 12 to purposefully initiatethe delivery of a sensory cue by interacting with an input mechanism(e.g., a programmer or a button on device 16 or another device) may beless useful. For example, if patient 12 exhibits motion impairment,patient 12 may find it difficult to initiate the movement to activateexternal cue device 16 (e.g., via a button or another input mechanism).Thus, in some cases, therapy system 10 may improve a quality of life ofpatient 12. While akinesia is the movement disorder primarily discussedherein during the description of therapy system 10, as well as the othertherapy system examples herein, in other examples, the therapy systemsdescribed herein may be useful for treating other movement disorders orother conditions that may affect the patient's ability to move.

Sensing device 14 is electrically coupled to electrode array 18, whichis positioned on a surface of the cranium of patient 12 proximate to amotor cortex of brain 20. In the example shown in FIG. 1, sensing device14, via electrode array 18, is configured to generate an EEG signal thatindicates the electrical activity within the motor cortex of brain 20,which is indicative of whether patient 12 is in a rest state or amovement state. The signals from the EEG are referred to as “EEGsignals.” The motor cortex is defined by regions within the cerebralcortex of brain 20 that are involved in the planning, control, andexecution of voluntary motor functions, such as walking and liftingobjects. Typically, different regions of the motor cortex controldifferent muscles. For example, different “motor points” within themotor cortex may control the movement of the arms, trunk, and legs ofpatient. Accordingly, electrode array 18 may be positioned to sense theEEG signals within particular regions of the motor cortex depending onwhat type of therapy the system 10 is designed to deliver. For example,if patient 12 has difficulty initiating movement of arms, electrodearray 18 may be positioned to sense the EEG signals at a motor pointthat is associated with the movement of the arms in order to detect thepatient's arm movement, attempted arm movement or intention to movearms. In other examples, electrode array 18 may be positioned proximateto other relevant regions of brain 20, such as, but not limited to, thesensory motor cortex, cerebellum or the basal ganglia.

An EEG is typically a measure of voltage differences between differentparts of brain 20, and, accordingly, electrode array 18 may include twoor more electrodes. Sensing device 14 may measure the voltage across atleast two electrodes of array 18. As described in further detail below,in one example, sensing device 14 includes a processor that determineswhether the EEG signals indicate patient 12 is in a movement state, andif so, controls external cue device 16 to deliver a cue to patient 12 tohelp patient 12 initiate movement or maintain movement. In one example,a processor within sensing device 14 determines whether the alphafrequency band component of the EEG signal detected within the occipitalcortex of patient 12 indicates whether patient 12 is in a relaxed state,indicating a lack of movement or a lack of an intention to move, or amovement state, which indicates patient 12 intends to move, is intendingto move, is attempting to move or is moving.

It has been found that the alpha band component (referred to as the“alpha waves” of the EEG signal) exhibits a detectable increase inamplitude when patient 12 undergoes a transition from a movement stateto a relaxed state. Thus, if sensing device 14 detects a decrease in thepower level of the alpha waves, the EEG signal may indicate patient 12is intending on moving, and, thus, is in a movement state. In responseto detecting the movement state, sensing device 14 may deliver anexternal cue to patient 12 via external cue device 16. In anotherexample, sensing device 14 relays the EEG signals to another device,which includes a processor that determines the EEG signals indicatepatient 12 is in a movement state.

While certain symptoms of a patient's movement disorder may generatedetectable changes within a monitored EEG signal, the symptomatic EEGsignal changes are not indicative of a movement state or rest state, asthe terms are used herein. Rather than monitoring the EEG signal fordetecting a patient's symptom, sensing device 14 detects a volitionalintention by the patient to move or an actual volitional movement viathe EEG signals. Sensing device 14 detects an EEG signal (or other brainsignal) that is generated in response to a volitional patient movement(whether it is just the mere intention of the movement or actualmovement), which differs from an EEG signal that is generated because ofa symptom of the patient's condition. Thus, the EEG signals and otherbrain signals in the methods and systems described herein arenonsymptomatic. Furthermore, the EEG signal and other brain signals thatprovides the feedback to control a therapy device results from avolitional patient movement or intention to move, rather than anincidental electrical signal within the patient's brain that the patientdid not voluntarily or intentionally generate. Thus, sensing device 14detects a brain signal that differs from involuntary neuronal activitythat may be caused by the patient's condition (e.g., a tremor or aseizure).

External cue device 16 is any device configured to deliver an externalcue to patient 12. As previously described, the external cue may be avisual cue, auditory cue or somatosensory cue (e.g., a pulsedvibration). Visual cues, auditory cues or somatosensory cues may havedifferent effects on patient 12. For example, in some patients withParkinson's disease, an auditory cue may help the patients grasp movingobjects, whereas somatosensory cues may help improve gait and generalmobility. Although external cue device 16 is shown as an eyepiece wornby patient 12 in the same manner as glasses, in other examples, externalcue device 16 may have different configurations. For example, if anauditory cue is desired, an external cue device may take the form of anear piece (e.g., an ear piece similar to a hearing aid or head phones).As another example, if a somatosensory cue is desired, an external cuedevice may take the form of a device worn on the patient's arm or legs(e.g., as a bracelet or anklet), around the patient's waist (e.g., as abelt) or otherwise attached to the patient in a way that permits thepatient to sense a somatosensory cue. A device coupled to the patient'swrist may, for example, provide a pulse, pulsed vibration, or othertactile stimulus.

External cue device 16 includes receiver 22 that is configured tocommunicate with sensing device 14 via a wired or wireless signal.Accordingly, sensing device 14 may include a telemetry module that isconfigured to communicate with receiver 22. Examples of local wirelesscommunication techniques that may be employed to facilitatecommunication between sensing device 14 and receiver 22 of device 16include radiofrequency (RF) communication according to the 802.11 orBluetooth specification sets, infrared communication, e.g., according tothe IrDA standard, or other standard or proprietary telemetry protocols.

Upon detecting a movement state based on EEG signals, sensing device 14may transmit a signal to receiver 22. A controller within external cuedevice 16 may initiate the delivery of the external cue in response toreceiving the signal from receiver 22. In some cases, external cuedevice 16 may also include a motion detection element (or a motionsensor), such as an accelerometer, that determines when patient 12 hasstopped moving. In such examples, external cue device 16 may transmitthe signals from the motion detection element to sensing device 14,which may process the signals to determine whether patient 12 hasstopped moving. Alternatively, the motion detection element may beseparate from external cue device 16 and may transmit electrical signalsindicative of patient movement to sensing device 14.

Upon detecting patient 12 has stopped moving, sensing device 14 mayprovide a control signal to external cue device 16 via transmitter 22that deactivates the delivery of the cue. In other examples, externalcue device 16 may include a processor that process the signals from themotion detection element and a controller that deactivates the cuedelivery upon detecting patient 12 has stopped moving, i.e., is in arest state. For example, external cue device 16 may repeatedly deliver asensory cue to patient 12 until movement stoppage is detected. In someexamples the relevant determination for terminating the cue delivery maybe whether patient 12 has successfully initiated movement. For example,if patient 12 exhibits akinesia, therapy system 10 may be implemented tohelp patient 12 initiate movement, and once movement is initiated,further therapy may not necessarily be useful.

As described in further detail below with respect to FIG. 12, the motiondetection element of external cue device 16 or another motion detectionelement that is separate from external cue device 16 may also be used tomake an independent determination that patient 12 is in a movement state(e.g., confirm patient 12 is actually moving). This independentdetermination of whether patient 12 is in the movement state may beuseful for detecting false positive movement state detections andminimizing unnecessary delivery of therapy to patient 12. In effect, amotion detection element may support a cross-correlation with themovement state detected from the patient's brain signal to confirmmovement with greater confidence.

In addition, in some examples, a second determination as to whetherpatient 12 is in a movement state based on the motion detection elementmay also be used to further control external cue device 16, such as todeactivate device 16 if patient 12 is not in a movement state or delivertherapy according to a different set of therapy parameter values. Thedifferent set of therapy parameter values may be used to help control adifferent symptom of a movement disorder. For example, the initialtherapy delivery by external cue device 16 based on the EEG signals maybe used to help patient 12 initiate movement, and a second set oftherapy parameters may be implemented upon determining that patient 12is in fact in the movement state, e.g., to help improve patient gait.

Sensing device 14 may employ an algorithm to suppress false positives,i.e., the detection of a bioelectrical brain signal falsely indicating amovement state. For example, sensing device 14 may implement analgorithm that identifies particular attributes of the biosignal (e.g.,certain frequency characteristics of the biosignal) that are unique tothe patient's movement state. As another example, sensing device 14 maymonitor the characteristics of the biosignal in more than one frequencyband, and correlate a particular pattern in the power of the brainsignal within two or more frequency bands in order to determine whetherthe brain signal is indicative of the volitional patient input. Thespecific characteristics may include, for example, a pattern or behaviorof the frequency characteristics of the bioelectrical brain signal, andso forth.

FIG. 1B is a top view of the patient's head and illustrates an exampleelectrode array 18, which includes electrodes 24A-24E coupled togethervia connecting members 26. Electrodes 24A-24E may comprise any suitablesurface electrodes that may measure electrical activity within brain 20of patient 12. Although five electrodes are shown in FIG. 1B, in otherexamples, electrode array 18 may include any suitable number ofelectrodes. It may be desirable to minimize the number of electrodes24A-24E for aesthetic purposes, while maintaining enough electrodes24A-24E to generate a useful EEG for detecting a movement state ofpatient 12.

Connecting members 26 may be made out of any suitable flexible or rigidmaterial, such as, but not limited to stainless steel, titanium,silicone, polyimide or another polymer. Electrodes 24A-24E of array 18are arranged relative to each other in order to adapt to the curvatureof the patient's head, as well as cover a large enough portion of therelevant region of brain 20 to measure the electrical activity.Electrode array 18 may be flexible to adapt to the particular curvatureof a patient's head, or may have a predetermined curvature that is basedon the average curvature of multiple patients' heads. Electrode array 18may be positioned above the patient's scalp or implanted below thepatient's scalp. Electrode array 18 may be coupled to sensing device 14via wireless telemetry or via a wired connection (e.g., a cable orlead). In this way, sensing device 14 may sense brain signals of patient12 via electrodes 24A-24E of electrode array 18.

Electrodes 24A-24E may be attached to the patient's head via anysuitable technique. For example, a conductive adhesive, such as, but notlimited to, tragacanth gum, karaya gum, acrylates, and conductivelyloaded hydrogels may be used and positioned between electrodes 24A-24Eand the surface of the patient's head. A clinician may locate the targetsite for electrode array 18 on the patient's head via any suitabletechnique. The target site is typically selected to correspond to theregion of brain 20 that generates an EEG signal indicative of therelevant motion. As previously described, different parts of the motorcortex of brain 20 may correspond to different types of movement (e.g.,movement of an arm or leg). Thus, if the clinician is primarilyconcerned with detecting a movement state of the patient's legs, theclinician may select a target site on the cranium of patient 12 thatcorresponds to the region within the motor cortex associated with legmovement.

In one example, the clinician may initially place electrode array 18 onthe patient's head based on the general location of the target region(e.g., it is known that the motor cortex is a part of the cerebralcortex, which may be near the front of the patient's head) and adjustthe location of electrodes 24A-24E as necessary to capture theelectrical signals from the target region. In another example, theclinician may rely on the “10-20” system, which provides guidelines fordetermining the relationship between a location of an electrode and theunderlying area of the cerebral cortex.

In addition, if electrodes 24A-24E are used to detect movement ofspecific limbs (e.g., fingers, arms or legs) of patient 12, theclinician may locate the particular location for detecting movement ofthe specific limb via any suitable technique. In one example, theclinician may utilize an imaging device, such as magnetoencephalography(MEG), positron emission tomography (PET) or functional magneticresonance imaging (fMRI) to identify the region of the motor cortex ofbrain 20 associated with movement of the specific limb. In anotherexample, the clinician may map EEG signals from different parts of themotor cortex and associate the EEG signals with movement of the specificlimb in order to identify the motor cortex region associated with thelimb. For example, the clinician may attach electrodes 24A-24E over theregion of the motor cortex that exhibited the greatest detectable changein EEG signal at the time patient 12 actually moved the limb.

FIG. 2 is a schematic diagram of another example of therapy system 30,which includes external cue device 16, an implantable medical device(IMD) 32 coupled to an array 34 of implanted electrodes, and programmer38. IMD 32 is similar to sensing device 14 shown in FIG. 1, but isimplanted within patient 12. In the example of FIG. 2, IMD 32 may besubdurally implanted (e.g., in a hollowed-out or recessed area of theskull or under the skull) in patient 12. In other examples, IMD 32 maybe implanted in another region of patient 12, such as in a subcutaneouspocket in a chest cavity or back of patient 12. In some examples, ahousing of IMD 32 may include or otherwise define another electrode formeasuring the EEG signal. IMD 32 is configured to communicate withtransmitter 22 of external cue device via wireless communicationtechniques, such as RF telemetry techniques.

Electrode array 34 is also similar to electrode array 18 of FIG. 1, butis implanted within the head of patient 12. In some examples, electrodearray 34 may be surgically implanted under the dura matter of brain 20or within the cerebral cortex of brain 20 via a burr hole in a skull ofpatient 12, and electrically coupled to IMD 32 via one or more leads. IfIMD 32 is implanted in a region of patient 12 other than the head, thelead coupling the electrode array 34 to IMD 32 may be surgicallyimplanted through a burr hole in the skull and routed throughsubcutaneous tissue to the implanted IMD 32. In some cases, electrodes34 implanted closer to the target region of brain 20 may help generatean EEG signal that provides more useful information than an EEGgenerated via a surface electrode array 18 because of the proximity tobrain 20. The EEG signal that is generated from implanted electrodearray may also be referred to as an electrocorticograph (ECoG).

Programmer 38 may be a handheld computing device that permits aclinician to communicate with IMD 32 during initial programming of IMD32, and for collection of information and further programming duringfollow-up visits to the clinician's office. Programmer 38 supportstelemetry (e.g., RF telemetry or telemetry via the Medical ImplantCommunication Service (MICS)) with IMD 32 to, for example, download EEGdata or other data stored, and sometimes collected, by IMD 32 or uploadinformation (e.g., operating software) to IMD 32. Programmer 38 may alsobe a handheld computing device for use by patient 12 to interact withIMD 32. Patient 12 may also retrieve information collected by IMD 32 viapatient programmer 38.

Programmer 38 may also be configured to communicate with external cuedevice 16 via any of the aforementioned local wireless communicationtechniques, such as RF telemetry techniques. Patient 12 or a clinicianmay modify the external cues delivered by external cue device 16 withthe aid of programmer 38. For example, patient 12 may decrease orincrease the contrast or brightness of a visual cue, increase ordecrease the longevity of the visual cue, increase or decrease thevolume of an auditory cue, increase or decrease the intensity of asomatosensory cue (e.g., the intensity of vibration) and so forth.

Programmer 38 may include a user interface comprising an inputmechanism, such as a keypad or peripheral device (e.g., a stylus ormouse), and a display, such as a liquid crystal display (LCD) or a lightemitting diode (LED) display. In some examples, the display ofprogrammer 38 may comprise a touch screen display, and a user mayinteract with programmer 38 via the touch screen display. Programmer 38is not limited to a hand-held computing device, but in other examples,may be any sort of computing device, such as a tablet-based computingdevice, a desktop computing device, or a workstation.

FIG. 3 is a block diagram illustrating an example sensing device 14,which monitors an EEG signal via electrodes 24A-24E (FIG. 1B) ofelectrode array 18 and controls external cue device 16 to deliver a cueto patient 12 to help control the effects of a movement disorder, e.g.,to help initiate movement. Sensing device 14 includes EEG sensing module40, which is coupled to electrodes 24A-24E via leads 50A-50E,respectively, processor 42, telemetry module 44, memory 46, and powersource 48. Two or more of leads 50A-50E may be bundled together (e.g.,as separate conductors within a common lead body) or may includeseparate lead bodies.

EEG sensing module 40, processor 42, as well as other components ofsensing device 14 requiring power may be coupled to power source 48.Power source 48 may take the form of a rechargeable or non-rechargeablebattery. Processor 42 controls telemetry module 44 to exchangeinformation with programmer 38 and/or external cue device 16. In someexamples, sensing module 14 may include separate telemetry modules forcommunicating with programmer 38 and external cue device 16. Telemetrymodule 44 may operate as a transceiver that receives telemetry signalsfrom external cue device 16 and transmits telemetry signals to anexternal cue device 16. External cue device 16 may provide informationto sensing device 14, such as a confirmation that a cue was delivered topatient 12 or information regarding the operation of external cue device16, such as a battery level of external cue device 16.

In some examples, processor 42 stores monitored EEG signals in memory46, and/or transmits the values to programmer 38 via telemetry module44. Memory 46 of sensing device 14 may include any volatile ornon-volatile media, such as a random access memory (RAM), read onlymemory (ROM), non-volatile RAM (NVRAM), electrically erasableprogrammable ROM (EEPROM), flash memory, and the like. Memory 46 mayalso store program instructions that, when executed by processor 42,cause processor 42 and the components of sensing device 14 to providethe functionality ascribed to them herein, e.g., cause EEG sensingmodule 40 to monitor the EEG signal of brain 20. Accordingly,computer-readable media storing instructions may be provided to causeprocessor 42 to provide functionality as described herein.

EEG sensing module 40 includes circuitry that measures the electricalactivity of a particular region, e.g., motor cortex, within brain 20 viaelectrodes 24A-24E. EEG sensing module 40 may acquire the EEG signalsubstantially continuously or at regular intervals, such as at afrequency of about 1 Hz to about 200 Hz. EEG sensing module 40 includescircuitry for determining a voltage difference between two electrodes24A-24E, which generally indicates the electrical activity within theparticular region of brain 20. One of the electrodes 24A-24E may act asa reference electrode, and, with respect to IMD 32 (FIG. 2), a housingof IMD 32 may act as a reference electrode. An example circuit that EEGsensing module 40 may include is shown and described below withreference to FIGS. 15-20. In some cases, the EEG signals measured fromvia external electrodes 24A-24E may generate a voltage in a range ofabout 5 microvolts (μV) to about 100 μV.

The output of EEG sensing module 40 may be received by processor 42.Processor 42 may apply additional processing to the signals, e.g.,convert the output to digital values for processing and/or amplify theEEG signal. In some cases, a gain of about 90 decibels (dB) is desirableto amplify the EEG signals. In some examples, EEG sensing module 40 orprocessor 42 may filter the signal from electrodes 24A-24E in order toremove undesirable artifacts from the signal, such as noise fromelectrocardiogram (ECG) signals, electromyogram (EMG) signals, andelectro-oculogram signals generated within the body of patient 12.

Processor 42 may also control the frequency with which EEG sensingmodule 40 generates an EEG signal. Processor 42 may include any one ormore of a microprocessor, a controller, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), discrete logic circuitry or the like.The functions attributed to processor 42 herein may be embodied assoftware, firmware, hardware or any combination thereof.

Processor 42 also controls the delivery of an external cue to patient 12based on the output of EEG sensing module 40. In one example, processor42 determines whether the EEG signal indicates patient 12 is in a reststate or a movement state. If processor 42 determines the EEG signalindicates patient 12 is in a movement state, processor 42 may generate amovement indication. The movement indication may be a value, flag, orsignal that is stored or transmitted to indicate the movement state.Processor 42 may transmit the movement indication to receiver 22 ofexternal cue device 16, which, in response, may deliver the external cueto patient 12. In this way, the movement indication may be a controlsignal for activating external cue device 16. In some examples,processor 42 may record the movement indication in memory 46 for laterretrieval and analysis by a clinician. For example, movement indicationsmay be recorded over time, e.g., in a loop recorder, and may beaccompanied by the relevant EEG signal.

Processor 42 may determine whether the EEG signal from EEG sensingmodule 40 indicates patient 12 is in a movement state or a rest statevia any suitable technique. If processor 42 determines that the EEGsignal indicates patient 12 is in a rest state, the EEG signal likewiseindicates patient 12 is not in a movement state. As various examples ofsignal processing techniques that processor 42 may employ, the EEGsignals may be analyzed for a particular relationship of the voltage orcurrent amplitude of the EEG waveform to a threshold value, temporalcorrelation or frequency correlation with a template signal, orcombinations thereof. For example, the instantaneous or averageamplitude of the EEG signal over a period of time may be compared to anamplitude threshold. For example, in one example, when the amplitude ofthe EEG signal is greater than or equal to the threshold value,processor 42 may control external cue device 16 to deliver the externalcue to patient 12.

As another example, a slope of the amplitude of the EEG signal over timeor timing between inflection points or other critical points in thepattern of the amplitude of the EEG signal over time may be compared totrend information. A correlation between the inflection points in theamplitude waveform of the EEG signal or other critical points and atemplate may indicate a movement state or a rest state. Processor 42 mayimplement an algorithm that recognizes a trend of the EEG signals thatcharacterize a brain state that indicates patient 12 is intending onmoving. If the trend of the EEG signals matches or substantially matchesthe trend template, processor 42 may control external cue device 16 todeliver the external cue to patient 12.

As another example, processor 42 may perform temporal correlation bysampling the EEG signal with a sliding window and comparing the sampledwaveform with a stored template waveform. For example, processor 42 mayperform a correlation analysis by moving a window along a digitized plotof the amplitude waveform of EEG signals at regular intervals, such asbetween about one millisecond to about ten millisecond intervals, todefine a sample of the EEG signal. The sample window is slid along theplot until a correlation is detected between the waveform of thetemplate and the waveform of the sample of the EEG signal defined by thewindow. By moving the window at regular time intervals, multiple sampleperiods are defined. The correlation may be detected by, for example,matching multiple points between the template waveform and the waveformof the plot of the EEG signal over time, or by applying any suitablemathematical correlation algorithm between the sample in the samplingwindow and a corresponding set of samples stored in the templatewaveform.

Different frequency bands are associated with different activity inbrain 20. One example of the frequency bands is shown in Table 1 below:

TABLE 1 Frequency (f) Band Hertz (Hz) Frequency Information f < 5 Hz δ(delta frequency band) 5 Hz ≦ f ≦ 10 Hz α (alpha frequency band) 10 Hz ≦f ≦ 30 Hz  β (beta frequency band) 50 Hz ≦ f ≦ 100 Hz γ (gamma frequencyband) 100 Hz ≦ f ≦ 200 Hz  high γ (high gamma frequency band)

It is believed that some frequency bands of the EEG signal may be morerevealing of the patient's movement state than other frequency bands. Inone example, the patient's movement state is detected by looking atparticular frequency components of the EEG signal. Various frequencybands of the EEG signal are associated with particular stages ofmovement. For example, the alpha band from Table 1 may be more revealingof a rest state, in which patient 12 is awake, but not active, than thebeta band. EEG signal activity within the alpha band may attenuate withan increase or decrease in physical activity. A higher frequency band,such as the beta or gamma bands, may also attenuate with an increase ordecrease in physical activity. For example, the “high” gamma band, whichmay include a frequency band of about 100 Hz to about 200 Hz, such asabout 150 Hz, may be revealing of the patient's movement state. Therelative power levels within the high gamma band (e.g., about 100 Hz toabout 200 Hz) of an EEG signal, as well as other bioelectric signals,has been shown to be both an excellent biomarker for motion intent, aswell as flexible to human control. That is, a human patient may controlactivity within the high gamma band with volitional thoughts, e.g.,relating to initiating movement.

Either EEG sensing module 40 or processor 42 may tune the EEG signal toa particular frequency band that is indicative of the patient'sintention to move. In some examples, EEG sensing module 40 or processormay tune the EEG signal to the alpha and/or high gamma bands. The powerlevel within the selected frequency band may be indicative of whetherthe EEG signal indicates patient 12 is in a movement state. For example,a relatively low power level within the alpha band or a relatively highpower level within the high gamma band may indicate the movement state.The high gamma band component of the EEG signal or another bioelectricalsignal of interest may be easier to extract than the alpha bandcomponent because the gamma band includes less noise than the alphaband. The noise may be due to, for example, other bioelectrical signals.In another example, the ratio of power levels within two or morefrequency bands may be compared to a stored value in order to determinewhether the EEG signal indicates patient 12 is in a movement state.

In another example, the correlation of changes of power betweenfrequency bands may be compared to a stored value to determine whetherthe EEG signal indicates patient 12 is in a movement state. For example,if the power level within the alpha band (e.g., a mu wave power)decreases and indicates patient 12 is in a movement state, and within acertain amount of time or at substantially the same time, the powerlevel within the high gamma band of the EEG signal increases, processor42 may confirm that patient 12 is in the movement state. Thiscorrelation of changes in power of different frequency bands may beimplemented into an algorithm that helps processor 42 eliminate falsepositive detections of the movement state, i.e., by providingconfirmation that the low power level (e.g., as compared to a storedvalue or trend template) within the alpha band or high power levelwithin the gamma band (e.g., as compared to a stored value or trendtemplate) indicates patient 12 is in the movement state.

In some examples, the EEG signal may be analyzed in the frequency domainto compare the power level of the EEG signal within one or morefrequency bands to a threshold or to compare selected frequencycomponents of an amplitude waveform of the EEG signal to correspondingfrequency components of a template signal. The template signal mayindicate, for example, a trend in the power level within one or morefrequency bands that indicates patient 12 is in a movement state.Specific examples of techniques for analyzing the frequency componentsof the EEG signal are described below with reference to FIGS. 8 and 9.

In various examples, processor 42 may monitor different frequencycomponents of an EEG signal to determine whether patient 12 is in amovement state. A mu rhythm, which is also referred to as a “mu wave,”is one component of the EEG signal that is present in the alphafrequency band. Mu waves are a particular wave of electromagneticoscillations in the alpha frequency band. In some examples, processor 42monitors the mu rhythm to determine whether patient 12 is in a movementstate, and thus, whether to control external cue device 16 to deliver acue to patient 12. When the power level of the mu rhythm oscillations isrelatively high, the EEG signal may indicate patient 12 is in a reststate and is not in a movement state. On the other hand, when the powerlevel of the mu rhythm is relatively low in the alpha band, the EEGsignal may indicate patient 12 is in a movement state, e.g., is actuallymoving, thinking about moving or attempting to move.

FIG. 4 is a block diagram illustrating various components of externalcue device 16. External cue device 16 includes controller 52, cuegenerator 54, telemetry module 56, and power source 58. Power source 58may be similar to power source 48 of sensing device 14, and providespower to components of external cue device 16. Cue generator 54generates the external cue that is delivered to patient 12. In the caseof a visual cue, for example, cue generator 54 may include a lightsource. In the case of an auditory cue, cue generator 54 may includecomponents that generate a noise that is audible to patient 12. In thecase of a somatosensory cue, cue generator 54 may include componentsthat generate a vibration, cause external cue device 16 to noticeablychange in temperature to patient 12 or another sensory experience bypatient 12 (e.g., another tactile signal).

Controller 52 controls cue generator 54. For example, controller 52 maycontrol the initiation of an external cue by cue generator 54. In somecases, controller 52 may also control the deactivation of the deliveryof an external cue to patient 12. Controller 52 may include softwareexecuting on a processing device, hardware, firmware or combinationsthereof. For example, controller 52 may comprise any one or more of amicroprocessor, a controller, a DSP, an ASIC, a FPGA, discrete logiccircuitry or the like. The functions attributed to controller 52 hereinmay be embodied as software, firmware, hardware or any combinationthereof.

Controller 52 is configured to receive a control signal from sensingdevice 14 via telemetry module 56 (which may include a wired or wirelessconnection to telemetry module 44 of sensing device 14), which includesthe receiver 22 (FIG. 1). Telemetry module 56 may comprise receiver 22(FIG. 1A) or may otherwise be coupled to receiver 22. In some cases,controller 52 may also transmit signals to another device, e.g.,programmer 38, via telemetry module 56. For example, if power source 58has a low level of remaining power, controller 52 may alert patient 12by sending a signal to programmer 38. Other types of alerts are alsocontemplated, such as a visible alert or an audible alert that differsfrom the visual or auditory cue. In addition, controller 52 may alsosend a signal to programmer 38 or sensing device 14 each time anexternal cue is delivered to patient 12, and programmer 38 or sensingdevice 14 may record the signal in the respective memory. Alternativelyor in addition to storing data within a memory of programmer 38 orsensing device 14, external cue device 16 may include a memory.

FIG. 5 is a conceptual diagram of another example therapy system 60,which includes external sensing device 14 that communicates with IMD 62.Rather than delivering an external cue to patient 12 upon detectingpatient 12 is in a movement state, therapy system 60 deliversstimulation therapy to patient 12. In the example shown in FIG. 5, IMD62 delivers electrical stimulation therapy to a stimulation site withinbrain 20 in order to help mitigate the symptoms of movement disorders.The target stimulation site within brain 20 which may depend upon thephysiological condition that is being addressed by the electricalstimulation therapy. For example, suitable target therapy delivery siteswithin brain 20 for controlling a movement disorder of patient 12include the pedunculopontine nucleus (PPN), thalamus, basal gangliastructures (e.g., globus pallidus, substantia nigra or subthalamicnucleus), zona inserta, fiber tracts, lenticular fasciculus (andbranches thereof), ansa lenticularis, and/or the Field of Forel(thalamic fasciculus). The PPN may also be referred to as thepedunculopontine tegmental nucleus. However, the target therapy deliverysite may depend upon the patient disorder or condition being treated.

Electrical stimulation is delivered from IMD 62 to brain 20 byelectrodes 66A-66D, which are carried by implantable medical lead 64.Lead 64 may be any suitable type of lead, such as a paddle lead or alead having a cylindrical shaped body. At least some of the electrodes66A-66D may comprise ring electrodes. In other examples, at least someof the electrodes 66A-66D may comprise segmented or partial ringelectrodes, each of which extends along an arc less than 360 degrees(e.g., 90-120 degrees) around the outer circumference of lead 64. Theconfiguration, type, and number of electrodes 66A-66D illustrated inFIG. 5 are merely exemplary. Although four electrodes 66A-66D are shown,lead 64 may carry any suitable number of electrodes in other examples,such as, but not limited to, two electrodes, six electrodes or eightelectrodes. In addition, in some examples, multiple leads may be coupledto IMD 62 to deliver stimulation therapy to patient 12.

IMD 62 may deliver stimulation therapy to patient 12 according to one ormore therapy parameter values. The values for the therapy parameters maybe organized into a group of parameter values referred to as a “therapyprogram” or “therapy parameter set.” “Therapy program” and “therapyparameter set” are used interchangeably herein. In the case ofelectrical stimulation, the therapy parameters may include an electrodecombination, and an amplitude, which may be a current or voltageamplitude, and, if IMD 62 delivers electrical pulses, a pulse width, anda pulse rate for stimulation signals to be delivered to the patient. Anelectrode combination may include a selected subset of one or moreelectrodes 66A-66D. The electrode combination may also refer to thepolarities of the electrodes in the selected subset. By selectingparticular electrode combinations, a clinician may target particularanatomic structures within brain 20 of patient 12. In some cases, IMD 62may deliver stimulation to patient 14 according to as program group thatincludes more than one therapy program. The stimulation signalsaccording to the different therapy programs in a therapy group may bedelivered on a time-interleaved basis or substantially simultaneously.

In one example, IMD 62 delivers electrical stimulation to a brain stemof patient 12, where the stimulation parameter values include a voltageamplitude of about 4 volts, a frequency of about 100 Hz, and a pulserate of about 200 microseconds (μs). However, other stimulationparameter values may be useful, depending on the particular targetstimulation site within patient 12. For example, an example range ofelectrical stimulation parameter values likely to be effective in deepbrain stimulation, for example, are listed below.

1. Frequency: between approximately 0.5 Hz and approximately 500 Hz,such as between approximately 5 Hz and 250 Hz, or between approximately70 Hz and approximately 120 Hz.

2. Amplitude: between approximately 0.1 volts and approximately 50volts, such as between approximately 0.5 volts and approximately 20volts, or approximately 5 volts. In other examples, a current amplitudemay be defined as the biological load in the voltage is delivered.

3. Pulse Width: between approximately 10 microseconds and approximately5000 microseconds, such as between approximately 100 microseconds andapproximately 1000 microseconds, or between approximately 180microseconds and approximately 450 microseconds.

Other ranges of therapy parameter values may be used when the therapy isdirected to other tissues. While stimulation pulses are described,stimulation signals may be of any forms such as sine waves or the like.

An example range of electrical stimulation parameter values likely to beeffective in treating chronic pain, e.g., when IMD 62 is configured todeliver spinal cord stimulation, is provided below. Again, whilestimulation pulses are described, stimulation signals may be of anyforms such as sine waves or the like.

1. Frequency: between approximately 0.5 Hz and approximately 500 Hz,such as between approximately 5 Hz and approximately 250 Hz, or betweenapproximately 10 Hz and approximately 50 Hz.

2. Amplitude: between approximately 0.1 volts and approximately 50volts, such as between approximately 0.5 volts and 20 volts, such asabout 5 volts. In other examples, a current amplitude may be defined asthe biological load in the voltage is delivered.

3. Pulse Width: between approximately 10 microseconds and approximately5000 microseconds, such as between approximately 100 microseconds andapproximately 1000 microseconds, or between approximately 180microseconds and approximately 450 microseconds.

A proximal end of lead 64 may be directly or indirectly electrically andmechanically coupled to a connector block of IMD 62. In particular,conductors disposed within a lead body of lead 64 electrically connectsstimulation electrodes 66A-66D located adjacent to distal end 64B oflead 64 to IMD 62. In other examples, multiple leads may be attached toIMD 62. In the example shown in FIG. 5, IMD 62 is an electricalstimulator implanted within patient 12. For example, IMD 62 may besubcutaneously implanted in the body of patient 12 (e.g., in a chestcavity, lower back, lower abdomen, buttocks or brain 20 of patient 12).IMD 62 provides a programmable stimulation signal (e.g., in the form ofelectrical pulses or substantially continuous-time signals) that isdelivered to a target stimulation site within brain 20 by one or morestimulation electrodes 66A-66D carried by implantable medical lead 64.The stimulation administered by IMD 62 to brain 20 may be selected basedon the specific movement disorder that is to be controlled by therapysystem 60 and the effect of the stimulation on other parts of brain 20.

Lead 64 may be implanted within brain 20 or another target stimulationsite within brain 20 of patient 12 via any suitable technique. In theexample shown in FIG. 5, lead 64 is implanted through a cranium ofpatient 12. For example, in one example, lead 64 may be surgicallyimplanted through a burr hole in a skull of patient 12, where lead 64extends between IMD 62 and target site within brain 20 through the skulland scalp. Alternatively, lead 64 may be surgically implanted through aburr hole in the skull and routed through subcutaneous tissue to IMD 62.In other examples, therapy system 60 may include an external stimulatorthat is coupled to percutaneous leads that are implanted within patient12.

Sensing device 14 may monitor an EEG signal via external sensingelectrode array 18 and process the signals to determine if the signalsindicate patient 12 is in a movement state. In the example shown in FIG.5, sensing device 14 is coupled to external electrode array 18 viaexternal lead 67. In other examples, sensing device 14 may be wirelesslycoupled to external electrode array 18. Upon detecting an EEG signalindicative of prospective movement, sensing device 14 may provide aninput to IMD 62 via wireless telemetry, such as with RF communicationtechniques. In response to receiving the input from sensing device 14,IMD 62 may control therapy delivery to patient 12, such as initiatingthe delivery of electrical stimulation to patient 12 or adjusting one ormore stimulation parameter values. Stimulation therapy may be deliveredto patient 12 according to a therapy program, which defines one or morestimulation parameter values.

In this way, sensing device 14 and IMD 62 define a responsive therapysystem for providing on demand stimulation or stimulation adjustment topatient 12. Providing stimulation on demand, when movement-specificactivation is desired, may be more beneficial to patient 12 thanproviding continuous or substantially continuous stimulation to patient12. In some cases, continuous or substantially continuous delivery ofstimulation to the brain 20 may interfere with other brain functions,such as activity within subthalamic nucleus, as well as therapeutic deepbrain stimulation in other basal ganglia sites. In addition, providingstimulation intermittently or upon the sensing of movement by patient 12may be a more efficient use of energy, particularly given finite batterypower resources that may be used by IMD 62 or other components.Delivering movement order-related stimulation on demand, e.g., whenpatient 12 is in a movement state may help conserve the power sourcewithin IMD 62, which may be an important consideration with an implantedelectrical stimulator.

It has also been found that patient 12 may adapt to deep brainstimulation provided by IMD 62 over time. That is, a certain level ofelectrical stimulation provided to brain 20 may be less effective overtime. This phenomenon may be referred to as “adaptation.” As a result,any beneficial effects to patient 12 from the deep brain stimulation maydecrease over time. While the electrical stimulation levels (e.g.,amplitude of the electrical stimulation signal) may be increased toovercome such adaptation, the increase in stimulation levels may consumemore power, and may eventually reach undesirable or harmful levels ofstimulation.

When stimulation is provided on demand, rather than continuously orsubstantially continuously, the rate at which patient adaptation to thetherapy, whether electrical stimulation, drug delivery or otherwise, mayoccur may decrease. Similarly, when one or more stimulation parametervalues (e.g., amplitude, voltage or frequency) are increased on demand,when a patient movement state is detected, both the rate at whichpatient 12 adapts to the stimulation therapy and the power consumed byIMD 62 may decrease as compared to continuous or substantiallycontinuous stimulation at the elevated parameter values. Thus, therapysystem 60 enables the therapy provided to patient 12 via IMD 62 to bemore effective for a longer period of time as compared to systems inwhich therapy is delivered continuously or substantially continuously topatient 12. In addition, providing therapy on demand may help reduce thepower requirements of IMD 62.

IMD 62 may also be configured to deliver stimulation to other regionswithin patient 12, in addition to or as an alternative to deliveringstimulation to brain 20. As examples, IMD 62 may deliver electricalstimulation therapy to the spinal cord of patient 12, nerves, muscles ormuscle groups of patient 12, or another suitable site within patient 12in order to help patient 12 better control muscle movement. In someexamples, after determining that an EEG signal indicates patient 12 in amovement state, sensing device 14 may provide input to IMD 62, which mayinitiate functional electrical stimulation (FES) or transcutaneouselectrical stimulation (TENS) of a muscle or muscle group of patient 12in order to help initiate movement or help patient 12 control movementof a limb or other body part. In the case of FES, IMD 62 may beimplanted to deliver stimulation to a muscle, rather than the brain 20of patient 12, as shown in FIG. 5. Alternatively, IMD 62 may take theform of one or more microstimulators implanted within a muscle ofpatient 12.

In other examples, IMD 62 may be configured to deliver a sensory cue topatient 12. For example, IMD 62 may deliver stimulation to a visualcortex of brain 20 of patient 12 in order to simulate an external visualcue. Stimulating the visual cortex may generate a visible signal topatient 12 that provides a substantially similar effect as an externalvisual cue. A sensory cue provided via IMD 62, however, may be morediscreet than a sensory cue provided by external cue device 16.

In other examples, the EEG signal sensed by sensing device 14 may beused in a therapy system to control other types of therapy. For example,fluid (e.g., a drug) may be delivered to one or more regions of brain20, the spinal cord, muscle, muscle group or another site within patient12 in order to help patient 12 initiate muscle movement. As anotherexample, a sensory cue may be delivered to patient via an externaldevice or an implanted device to help patient 12 initiate musclemovement. In general, the delivery of electrical stimulation, drugtherapy or sensory cue may help alleviate, and in some cases, eliminatesymptoms associated with movement disorders. Furthermore, althoughexternal sensing device 14 is shown in FIG. 5, in other examples,therapy system 60 may include an implanted sensing device 32 andimplanted electrode array 34, as shown in FIG. 2. In some examples, theimplanted sensing device 32 and IMD 62 may be incorporated within acommon housing and may, in some examples, share electrodes or leads thatcarry electrodes for sensing EEG signals and delivering electricalstimulation therapy to patient 12.

FIG. 6 is a block diagram illustrating various components of IMD 62 andan implantable medical lead 64 carrying one or more sense and/orstimulation electrodes. IMD 62 includes therapy delivery module 70,processor 72, telemetry module 74, memory 76, and power source 78. Insome examples, IMD 62 may also include a sensing circuit (not shown inFIG. 2), e.g., for sensing brain signals (e.g., EEG or ECoG signals) orother physiological parameters of patient 12. Implantable medical lead64 is coupled to therapy module 70 either directly or indirectly, e.g.,via an extension. In particular, electrodes 66A-66D, which are disposednear a distal end of lead 64, are electrically coupled to a therapydelivery module 70 of IMD 62 via conductors within lead 64.

In one example, an implantable signal generator or other stimulationcircuitry within therapy delivery module 70 generates and deliverselectrical signals (e.g., pulses or substantially continuous-timesignals, such as sinusoidal signals) to a target stimulation site withinpatient 12 via at least some of electrodes 66A-66D under the control ofprocessor 72. The signals may be delivered from therapy delivery module70 to electrodes 66A-66D via a switch matrix and conductors carried bylead 64 and electrically coupled to respective electrodes 66A-D.However, in some examples, electrodes 66A-66D may be independentlyactivatable (e.g., stimulation may be selectively delivered to one ormore electrodes 66A-66D at a time) without the aid of a switch matrix.

The implantable signal generator may be coupled to power source 78.Power source 78 may take the form of a small, rechargeable ornon-rechargeable battery, or an inductive power interface thattranscutaneously receives inductively coupled energy. In the case of arechargeable battery, power source 78 similarly may include an inductivepower interface for transcutaneous transfer of recharge power.

Processor 72 may include any one or more microprocessors, controllers,DSPs, ASICs, FPGAs, discrete logic circuitry, or the like. The functionsattributed to processor 72 herein may be embodied as software, firmware,hardware or any combination thereof. Processor 72 controls theimplantable signal generator within therapy delivery module 70 todeliver electrical stimulation therapy according to selected stimulationparameter values, which may be stored as a set of parameter values in atherapy program. Specifically, processor 72 may control therapy deliverymodule 70 to deliver electrical signals with selected amplitudes, pulsewidths (if applicable), and rates specified by the therapy programs,which may be stored within memory 76. In addition, processor 72 may alsocontrol therapy delivery module 70 to deliver the stimulation signalsvia selected subsets of electrodes 66A-66D with selected polarities. Forexample, electrodes 66A-66D may be combined in various bipolar ormulti-polar combinations to deliver stimulation energy to selectedsites, such as nerve sites adjacent the spinal column or brain 20.

Processor 72 may also control therapy delivery module 70 to deliver eachstimulation signal according to a different program, therebyinterleaving programs to simultaneously treat different symptoms orprovide a combined therapeutic effect. For example, in addition totreatment of one symptom, such as akinesia, IMD 62 may be configured todeliver stimulation therapy to treat other symptoms such as pain orincontinence.

Memory 76 of IMD 62 may include any volatile or non-volatile media, suchas a RAM, ROM, NVRAM, EEPROM, flash memory, and the like. In someexamples, memory 76 of IMD 62 may store multiple sets of stimulationparameter values that are available to be selected by patient 12 orclinician via programmer 38 (FIG. 3) for delivery of stimulationtherapy. For example, memory 76 may store stimulation parameter valuestransmitted by programmer 38 (FIG. 1). Memory 76 also stores programinstructions that, when executed by processor 72, cause IMD 62 todeliver neurostimulation therapy. Accordingly, computer-readable mediastoring instructions may be provided to cause processor 72 to providefunctionality as described herein.

Processor 72 may also control telemetry module 74 to exchangeinformation with an external programmer, such as programmer 38, andsensing device 14 or 32 (FIGS. 1 and 2) by wireless telemetry. Forexample, sensing device 14 may transmit a control signal to processor 72upon detecting patient 12 is in a movement state, and, in response toreceiving the control signal, processor 72 may control therapy module 70to deliver stimulation to patient 12 or increase or otherwise adjust theelectrical stimulation parameters (e.g., pulse width, pulse rate,amplitude, and so forth).

While FIGS. 5 and 6 relate to examples in which IMD 62 and sensingdevice 14 are disposed in separate housings, in other examples, IMD 62and sensing device 14 may be incorporated into a common housing. Forexample, IMD 62 and sensing device 14 may be incorporated into a commonhousing that is implanted within patient 12 or a common housing that iscarried external to patient 12. As one example, IMD 62 may be modifiedto include EEG sensing module 40 that is coupled to processor 72 of IMD62, and processor 72 may be configured to detect a movement state fromthe EEG signal monitored by EEG sensing module 40. Furthermore, if IMD62 and sensing device 14 are incorporated into a common housing, IMD 62and sensing device 14 may share processors, memories, and so forth, aswell as one or more leads that carry electrodes for sensing EEG signalsand electrodes for delivering stimulation.

FIG. 7 illustrates a flow diagram of a technique for controlling atherapy device, such as an external cue device 16 (FIG. 1) or IMD 62(FIG. 5) based on an EEG signal. Sensing device 14 monitors the EEGsignal within the motor cortex of brain 20 via surface electrode array18 continuously or at regular intervals (80). In other examples, sensingdevice 14 may monitor the EEG signal within another part of brain 20.While external sensing device 14 is primarily referred to throughout theremainder of the application, the techniques described herein withrespect to FIG. 7, as well as the other figures, may also be implementedby implanted sensing device 32. In addition, while EEG signals areprimarily referred to with respect to the description of FIGS. 7-12, themovement state of patient 12 may also be detected based on other typesof brain signals, such as a signal generated from measured fieldpotentials within one or more regions of a patient's brain and/or actionpotentials from single cells within the patient's brain

Processor 42 (FIG. 3) of sensing device 14 receives sensor signals fromEEG sensing module 40 and processes the EEG signals to determine whetherthe EEG signals indicate patient 12 is in a movement state (82). Asignal processor within processor 42 or sensing module 40 of sensingdevice 14 may determine whether the EEG signals are indicative of amovement state using any suitable technique, such as the techniquesdescribed above (e.g., voltage, amplitude, temporal correlation orfrequency correlation with a template signal, or combinations thereof).If the EEG signals do not indicate a movement state, sensing module 40may continue monitoring the EEG signal under the control of processor 42(80). If the sensor signals indicate a movement state, processor 42 maycontrol a therapy device (84). As previously discussed, the therapydevice may be external cue device 16 shown in FIG. 1, an electricalstimulation device (e.g., IMD 62) or a fluid delivery device.

For example, in the case of external cue device 16, processor 42 ofsensing device 14 may provide a signal to controller 52 of external cuedevice 16 via telemetry module 56, and controller 52 may cause cuegenerator 54 to generate and deliver a visual cue to patient 12. Asanother example, in the case of IMD 62 of FIGS. 5 and 6, upon processingthe signals received from sensing module 40 and determining that patient12 is in a movement state, processor 42 may provide a signal toprocessor 72 of IMD 62 via the respective telemetry modules 46 and 74.Processor 72 of IMD 62 may then initiate therapy delivery via therapymodule 70 or adjust therapy (e.g., increase an intensity of therapy inorder to help patient 12 initiate muscle movement).

In some examples of the therapy systems described herein, the therapysystem may provide feedback to patient 12 to indicate that the movementstate was detected and therapy was adjusted accordingly. For example,the sensory cortex of brain 16 may be stimulated to provide thesensation of a visible light. Other forms of sensory feedback are alsopossible, such as an audible sound or a somatosensory cue. In someexamples, programmer 38 may include a feedback mechanism, such a LED,another display or a sound generator, which indicates that the therapysystem received the volitional patient input and that the appropriatetherapy adjustment action was taken. By learning which patient actionsresulted in the movement state being detected and therapy being adjustedaccordingly, patient 12 may learn to control the EEG signal to triggertherapy adjustment.

FIG. 8 is a flow diagram of an example technique for controlling atherapy device based on one or more frequency characteristics of an EEGsignal. Sensing device 14 monitors an EEG signal from the motor cortexof brain 20 of patient 12 via surface electrode array 18 (FIG. 1) (80).The discussion of FIG. 8 will primarily refer to sensing device 14.However, in other examples, IMD 32 (FIG. 2) may measure the EEG signalvia an implanted electrode array 34 (FIG. 2).

A signal processor within processor 42 of sensing device 14 analyzes thestrength of the monitored EEG signal within a relatively low frequencyband (e.g., the alpha or delta frequency bands from Table 1 above) (86).If the power level (also referred to as “energy” or an indication of thesignal strength) within the low frequency band is relatively low (88),the brain signals may indicate that the power level is ramping up to ahigher frequency band (e.g., the beta or gamma frequency bands fromTable 1 above) and patient 12 is in a movement state. That is, an EEGsignal that includes a relatively low power level within a low frequencyband may be indicative of the movement state of patient 12. The “low”power level may be determined during a trial stage, which is describedwith reference to FIG. 14. The power level within the low frequency bandmay be compared to a threshold value to determine whether the powerlevel indicates patient 12 is in a movement state. A power level fallingbelow the threshold value may indicate patient 12 is in a movementstate.

Alternatively, processor 42 may perform a temporal analysis of the powerwithin the low frequency band to determine whether the power within thelow frequency band increased or decreased relatively quickly over time.A decrease in the power in the low frequency band over time may indicatepatient 12 is entering a movement state because the power level isramping up to a higher frequency band, which is associated withmovement. In one example, processor 42 compares the strength of an EEGsignal within the low frequency band to a mean signal strength of theEEG signal from a previous time span, such as about 5 seconds to about20 seconds, in order to determine whether the power within the lowfrequency band increased or decreased relatively quickly over time.

In response to detecting the signal indicative of prospective movement,processor 42 may control a therapy device (84), such as external cuedevice 16 shown in FIG. 1, an electrical stimulation device or a fluiddelivery device. On the other hand, if the power in the lower frequencyband is relatively high, patient 12 may be in a rest state, andprocessor 42 may not take any action, while sensing module 40 maycontinue monitoring the EEG signal (80).

Rather than monitoring a power of a low frequency band, in someexamples, sensing module 40 of sensing device 14 may monitor the powerlevel within a high frequency band (e.g., gamma or beta bands), and anincreased power level in the high frequency band may indicate patient 12is in a movement state. In general, if the strong (i.e., relatively highpower) signals fall within a high frequency band (e.g., the beta orgamma bands from Table 1), or otherwise do not fall within the lowerfrequency band, processor 42 may generate a control signal to activateor otherwise control a therapy delivery device (84). As anotheralternative, sensing module 38 may monitor the power level within boththe low and high frequency bands.

As another example, sensing module 40 may monitor both the power levelwithin a low frequency band and a high frequency band. A correlation inthe pattern of power levels within the low and high frequency bands mayindicate patient 12 is in a movement state. For example, sensing module40 may implement an algorithm that determines whether the power levelwithin the low frequency band decreases, and, at substantially the sametime or during a subsequent time period, determines whether the powerlevel within the high frequency band decreases. The correlation orassociation of the trends in power level or power levels within morethan one frequency band may help suppress false positives, i.e., falsedetections of the movement state, by providing two avenues for detectingthe movement state of patient 12.

FIG. 9 is a flow diagram of another example of the technique shown inFIG. 8. Processor 42 of sensing device 14 may monitor the mu rhythm inthe alpha band of the EEG signal from the motor cortex of brain 20 ofpatient 12 (80, 90). As previously described, a mu rhythm (or a mu wave)is a particular wave of electromagnetic oscillations in the alphafrequency band of an EEG signal. Processor 42 may analyze the powerlevel of the mu rhythm in the alpha band. For example, processor 42 maydetermine whether the power level of the mu rhythm is above a threshold(92), which may be determined during a trial phase of the therapy system10. If the power level of the mu rhythm is above the threshold, the EEGsignal may indicate patient 12 is in a rest state. In some cases, whenpatient 12 is in a rest state, therapy is typically not necessary tohelp patient 12 initiate movement or otherwise control symptoms of amovement disorder. Thus, processor 42 may continue monitoring the murhythm in the alpha band of the EEG signal from the occipital cortex(80, 90). However, if the power level of the mu rhythm is below thethreshold, the mu rhythm may indicate patient 12 is in a movement state,and processor 84 may control a therapy device (84).

In each of the examples described above in which processor 42 of sensingdevice 14 provides a control signal that is transmitted to external cuedevice 16 or IMD 62 to initiate therapy delivery to patient 12 orotherwise adjust therapy delivery to patient 12 in response to detectinga movement state, the therapy delivery may be initiated or delivered atadjusted therapy parameter values for a predetermined amount of time oruntil processor 42 receives an indication that patient 12 hassuccessfully initiated movement or has stopped moving, depending uponthe type of movement disorder that is treated. For example, if therapysystem 10 is used to control akinesia, the therapy may be deactivatedafter patient 12 has successfully initiated movement or after apredetermined amount of time.

The predetermined amount of time may be selected to be sufficient toinitiate patient movement or otherwise gain control of muscle movement.For example, if initiation of patient movement is desired, thepredetermined amount of time may be relatively short (e.g., less thanfive seconds). On the other hand, if therapy system 10 is used tocontrol gait freeze, which may occur at many possible points during amovement state, the therapy may be deactivated after patient 12 hasstopped moving, i.e., has entered a rest state. In the case of amovement disorder, it may be useful to deliver therapy to patient 12 fora defined period of time, rather than substantially continuously, inorder to help patient 12 initiate movement, while conserving the powersource 58 of external cue device 16 or power source 78 of IMD 62. Asdescribed in further detail below, a motion sensor may be used todetermine when therapy should be deactivated or otherwise adjusted. Themotion sensor may indicate, for example, that patient 12 hassuccessfully initiated movement or is in a rest state.

In some therapy systems, therapy parameter values may be modifieddepending upon the type of movement that patient 12 is intending oninitiating. For example, if patient 12 is afflicted with tremor, andstimulation therapy is provided to patient 12 to help alleviate thetremor, a greater amplitude or pulse rate of stimulation frequency maybe delivered if patient 12 is intending on performing a task thatrequires better control of movement (e.g., signing his name on a pieceof paper with a pen) compared to when patient 12 is performing a taskthat requires less control of movement (e.g., intending on reaching foran object with his arm). That is, because more precise movement anddexterity may be required for holding a pen and writing compared toreaching for an object, the stimulation therapy parameter values (e.g.,pulse amplitude, pulse rate, electrode configuration, and so forth)necessary to reduce the tremor for those two actions may differ.Similarly, in the case of a sensory cue, a different sensory cue may bemore useful to patient 12 if patient 12 is intending on walking comparedto when patient 12 is intending on lifting his arm.

Accordingly, in some examples, therapy parameter values may be selected,i.e., therapy may be “titrated” depending on the type of movement thatpatient 12 intends on undertaking or is actually undertaking. Sensingdevice 14, external cue generator 16 or IMD 62 may store a plurality oftherapy programs, which define a set of therapy parameter values, fordifferent types of movement. The types of movement may be distinguishedon the level of activity, which may be reflected by the intensity levelof certain frequency band components of the EEG signal.

FIG. 10 is a flow diagram of an example technique that may be employedto titrate therapy based on the strength of an EEG signal within aparticular frequency band, which may indicate the type of movementpatient 12 intends on undertaking or is actually undertaking. While thehigh gamma band (e.g., between about 100 Hz to about 200 Hz) isprimarily referred to in the description of FIG. 10, in other examples,other frequency bands that are revealing of the patient's intention tomove may also be implemented in the technique shown in FIG. 10.

Sensing device 14 monitors an EEG signal of brain 20 of patient 12 (80),and processor 42 of sensing device 14 extracts the high gamma bandcomponent of the EEG signal in order to analyze the signal strengthwithin the high gamma band (94). In some examples, sensing device 14 mayfilter out the high gamma band component of the EEG signal prior totransmitting the brain signal to processor 42. Processor 42 maydetermine the intensity (or strength) of the EEG signal within the highgamma band (96). Based on the intensity within the high gamma band,processor 42 may determine what type of motion patient 12 is intendingon initiating, and titrate therapy accordingly (98).

The intensity within the high gamma band or within another frequencyband may be associated with a particular motion or degree of motion(e.g., relative levels of activeness or precision) during a trial stage.For example, during a trial stage, sensing device 14 may monitor the EEGsignal that is generated when patient 12 initiates a variety ofdifferent movements, such as movement of an arm, finger, leg, and soforth. Based on the EEG signal associated with each movement, aclinician, with the aid of a computing device, may associate a movementwith the intensity level within the high gamma band at the time patient12 initiated the thoughts directed to initiating the respectivemovement. The high gamma band intensity level and the associatedmovement may be recorded in memory 46 of sensing device 14.

FIG. 11 is a flow diagram of an example technique for deactivating oradjusting therapy delivery in response to detecting patient 12 hasstopped moving or has successfully initiated movement. Processor 42 ofsensing device 14 may monitor the EEG signal from the relevant region ofthe patient's brain 20, which may depend upon the type of movement beingdetected (80). Processor 42 may determine whether the EEG signalindicates patient 12 is in a movement state (100) using any of thetechniques described above. Upon determining patient 12 is in a movementstate, processor 42 may control therapy device (84). For example,processor 42 may generate a control signal that activates therapydelivery or adjusts therapy delivery.

Processor 42 may then determine whether patient 12 in a rest state(102). In other examples, processor 42 may be configured to decrease theintensity of therapy (e.g., the voltage or current amplitude ofelectrical stimulation, the frequency of electrical stimulation, thebolus size of a drug, the frequency of the bolus delivery, and the like)or stop therapy upon the detection of the successful initiation ofpatient movement. An indication that patient 12 has initiated movementor stopped moving (e.g., is in a rest state) may be generated anysuitable way. In some examples, processor 42 may determine whetherpatient 12 is no longer in the movement state (i.e., is in the reststate) based on a signal that is independent of brain signals. Asdescribed in further detail below, the rest state or the initiation ofmovement may be detected via any suitable technique, such as bydetecting gross movement from a motion sensor (e.g., an accelerometer)or based on the EEG signals. If the rest state is not detected,processor 42 may continue controlling the therapy device (84). However,if the rest state is detected, processor 42 may generate a controlsignal to stop or adjust the delivery of therapy (104).

In other examples, processor 42 may monitor the EEG signals to determinewhether the EEG signals indicate patient 12 is in a rest state. In thecase of a mu rhythm, for example, processor 42 may determine whether thepower level of the mu rhythm exceeds the threshold, which may indicatepatient 12 is in a rest state. If the therapy delivery is deactivatedupon detecting patient 12 has successfully initiated movement, processor42 may monitor the EEG signal and analyze the signal to determinewhether patient 12 is still in a movement state a predetermined amountof time after the initiation of the movement state was detected, such asabout 10 seconds to about two minutes. The period of time for detectingthe initiation of movement should be selected to provide patient 12 withenough time to actually initiate movement.

In some examples, processor 42 of sensing device 14 (or processor 72 ofIMD 62 or controller 52 of external cue device 16) may initially controltherapy delivery to patient 12, e.g., initiate therapy delivery oradjust therapy parameter values, based on an EEG signal (or other brainsignal) sensed by sensing device 14. Processor 42 may then make longerterm adjustments to therapy based on signals from another sensor, suchas a motion sensor. That is, upon determining that patient 12 is in amovement state based on EEG signals or other brain signals, processor 42may subsequently determine whether patient 12 is still in the movementstate, and, in some examples, the relative activity level of patient 12,to provide further control of therapy. The long term therapy adjustmentsmay include, for example, continuing therapy delivery to patient 12based on signals from a sensor that may indicate patient movementindependently of any EEG signals monitored by sensing device 14,deactivating therapy delivery to patient 12, or modifying one or moretherapy parameter values of the therapy that is currently beingdelivered to patient 12. In addition, by determining whether patient 12is in a movement state after the movement state is detected based on theEEG signals, processor 42 may confirm that the movement state wasproperly detected.

FIG. 12 is a flow diagram of an example technique that may beimplemented to control a therapy device in response to detecting patient12 is in a movement state based on an EEG signal (or other bioelectricalbrain signal) from sensing device 14 and a signal from a motion sensor.As with FIGS. 7-11, although FIG. 12 is described with respect toprocessor 42 of sensing device 14, in other examples, a processor ofanother device, such as external cue device 16, IMD 62 or implantedsensing device 32, may perform any part of the technique shown in FIG.12.

Processor 42 of sensing device 14 may monitor the EEG signal from therelevant region of the patient's brain 20, which may depend upon thetype of movement being detected (80). Processor 42 may determine whetherthe EEG signal indicates patient 12 is in a movement state (100) usingany of the techniques described above. Upon determining patient 12 is ina movement state, processor 42 may control a therapy device (84), suchas by generating a control signal that activates therapy delivery oradjusts therapy delivery for the movement state.

Processor 42 may then reference a motion sensor to determine whetherpatient 12 is in a movement state (106). The motion sensor generates asignal indicative of patient motion that is independent of the EEGactivity or other bioelectrical brain activity of patient 12. Forexample, processor 42 may determine the gross or relative activity levelof patient 12 based on the output from an accelerometer. The motionsensor may generate electrical signals that change at least one signalcharacteristic (e.g., a signal amplitude or frequency) as a function ofpatient motion. In some examples, processor 42 may compare an electricalsignal from the motion sensor to a baseline signal (e.g., a thresholdvalue of the signal amplitude or a slope or other component of thesignal) to determine whether the electrical signal indicates patient 12is in a movement state. The baseline signal may comprise, for example,the baseline signal of the motion sensor when patient 12 is in a reststate. The baseline signal may be adjusted over time to account forchanges in the baseline signal of the motion sensor when patient 12 isin a rest state. Accordingly, the baseline signal may not have a fixedcharacteristic (e.g., amplitude value).

The motion sensor may be positioned to detect movement of patient 12 andmay be implanted within patient 12 or may be external to patient 12.Examples of motion sensors are described with respect to FIG. 13. If themotion sensor indicates patient 12 is not a movement state (106),processor 42 may decrease the intensity of therapy delivery (e.g., byswitching to a different therapy program) or terminate therapy delivery(108), and continue monitoring the EEG signal (80). If the signalgenerated by the motion sensor indicates patient 12 is not in a movementstate, processor 42 may determine that the detection of the motion statebased on the EEG signal was a false positive, and, accordingly, therapydelivery to help patient 12 initiate or maintain motion may not benecessary.

If the motion sensor indicates patient 12 is in a movement state (106),processor 42 may continue therapy delivery or modify therapy delivery(e.g., deliver therapy according to a different therapy program) (110).In some examples, processor 42 may switch therapy programs or otherwiseadjust a therapy parameter value upon determining that patient 12 isstill in the movement state following the initial determination of themovement state based on the EEG signal (100). For example, afterdetermining patient 12 is in a movement state based on the EEG signals,processor 42 may generate a control signal that causes a therapy deviceto deliver therapy to patient 12 according to a first therapy program tohelp patient 12 initiate movement.

In some examples, upon determining that patient 12 is actually in amovement state based on the motion sensor (106), processor 42 maydetermine that patient 12 has successfully initiated movement, and,therefore, the first therapy program may no longer be as useful asanother therapy program, such as therapy program that helps improvepatient 12 gait or control of movement. Thus, upon determining thatpatient 12 is in a movement state based on the motion sensor (106),processor 42 may control a therapy device (e.g., by generating anothercontrol signal) to deliver therapy according to a second therapy programthat is different than the first therapy program. Processor 42 mayselect the second therapy program using any suitable technique. In someexamples, memory 46 of sensing device 14 or another device may store aplurality of therapy programs and associate each therapy program with anactivity level, e.g., an electrical signal or a range of electricalsignals generated by the motion sensor. Processor 42 may reference thestored therapy programs and select the therapy program that is bestassociated with the signal (or other input) received from the motionsensor. In this way, processor 42 may titrate therapy to patient 12based on the relative activity level of patient 12.

In other examples, upon determining that patient 12 is in a movementstate based on the motion sensor (106), processor 42 may control atherapy device (e.g., by generating another control signal) to continuedelivering therapy according the first therapy program with whichtherapy was delivered following detection of the movement state based onthe EEG signals.

In some examples, processor 42 may continue monitoring the EEG signalsor motion sensor signals to determine whether patient 12 is in amovement state or whether patient is in a rest state. As described withrespect to FIG. 11, in other examples, processor 42 may be configured todecrease an intensity of therapy (e.g., a frequency of therapy deliveryor an amplitude or pulse width of an electrical stimulation signal inthe case of stimulation therapy) or stop therapy upon the detection ofthe successful initiation of patient movement.

Confirming that patient 12 is in a movement state based on a signalother than the EEG signal, e.g., a motion sensor, may help preventunnecessary delivery of therapy and may provide a more robust titrationof therapy. For example, if patient 12 is intending to initiatemovement, such that the EEG signals monitored by processor 42 indicatepatient 12 is in a movement state, but patient 12 ultimately decides notto initiate movement, processor 42 may generate the control signal thatactivates therapy delivery or adjusts therapy delivery (84) althoughtherapy may not be necessary to initiate or maintain movement. Themotion sensor may help confirm that patient 12 followed through on anintent to move.

Processor 42 may reference the signal from the motion sensor todetermine whether the motion sensor indicates patient 12 is in amovement state (106) at any suitable time. For example, processor 42 mayreference the signal from the motion sensor within about 1 second toabout 10 minutes after processor 42 determines patient 12 is in amovement state based on the EEG signal (100). In some examples,processor 42 may determine when to reference the signal from a motionsensor based on information provided by a predictive filter, such as aKalman filter.

FIG. 13 is a schematic diagram illustrating motion sensor 116 that maybe used to monitor an activity level of patient 12 to determine whetherpatient 12 has initiated movement, stopped moving, and/or to confirmthat patient 12 is in a movement state. Processor 42 may monitor outputfrom motion sensor 116 immediately after therapy is delivered to patient12 or within a certain period of time after therapy is delivered, suchas about 5 seconds to about 10 seconds or longer. Signals generated bymotion sensor 116 may be sent to processor 42 of sensing device 14 viawireless signals or a wired connection, which may process the signals todetermine whether patient 12 has initiated movement or stopped moving,and provide a control signal to external cue device 16 or IMD 62 todeactivate therapy or decrease therapy delivery parameters (e.g.,stimulation amplitude, frequency, size of a drug bolus, etc.).Alternatively, the signals generated by motion sensor 116 may be sentdirectly to the therapy source, e.g., external cue device 16 or IMD 62via wireless signals or a wired connection.

Motion sensor 116 is an external device that may be attached to patient12 via a belt 118. Alternatively, motion sensor 116 may be attached topatient 12 by any other suitable technique, such as a clip that attachesto the patient's clothing, via a wristband, as shown with motion sensor120 or via a band attached to the patient's leg, as shown with motionsensor 122. Motion sensors 116, 120, 122 may each include sensors thatgenerate a signal indicative of patient motion, such as accelerometer ora piezoelectric crystal. Alternatively, a motion sensor may beintegrated with sensing device 14, external cue device 16, IMD 32, orIMD 62 or implanted within patient 12.

In addition to or instead of a motion sensor, a sensor that generates asignal that indicates a physiological parameter that varies as afunction of patient activity may be used to determine whether patient 12has successfully initiated movement or has stopped moving, depending onthe type of therapy deactivation signal desired. Suitable physiologicalparameters include heart rate, respiratory rate, electrocardiogrammorphology, respiration rate, respiratory volume, core temperature, amuscular activity level, subcutaneous temperature or electromyographicactivity of patient 12.

For example, in some examples, patient 12 may wear an ECG belt thatincorporates a plurality of electrodes for sensing the electricalactivity of the heart of patient 12. The heart rate and, in someexamples, ECG morphology of patient 12 may monitored based on the signalprovided by the ECG belt. Examples of suitable ECG belts for sensing theheart rate of patient 12 are the “M” and “F” heart rate monitor modelscommercially available from Polar Electro of Kempele, Finland. In someexamples, instead of an ECG belt, patient 12 may wear a plurality of ECGelectrodes (not shown in FIG. 11) attached, e.g., via adhesive patches,at various locations on the chest of patient 12, as is known in the art.An ECG signal derived from the signals sensed by such an array ofelectrodes may enable both heart rate and ECG morphology monitoring, asis known in the art.

As another example, patient movement may be detected via a respirationbelt, such as a plethysmograpy belt, that outputs a signal that variesas a function of respiration of the patient. An example of a suitablerespiration belt is the TSD201 Respiratory Effort Transducercommercially available from Biopac Systems, Inc of Goleta, Calif.Alternatively, a plurality of electrodes that direct an electricalsignal through the thorax of patient 12, and circuitry to sense theimpedance of the thorax, which varies as a function of respiration ofpatient 12, may used to detect a patient activity level, which indicateswhether patient 12 is in a movement state. In other examples, anindication that patient 12 has initiated movement or stopped moving maybe generated in other ways.

As previously described, processor 42 may determine whether an EEGsignal indicates patient 12 is in a rest state or a movement state byvoltage, amplitude, temporal correlation or frequency correlation with atemplate signal, monitoring the power level within a particularfrequency band of the EEG signal, or combinations thereof. The EEGsignal characteristic that indicates patient 12 is in a rest state or amovement state may differ between patients.

FIG. 14 is a flow diagram of a technique for determining the EEG signal(or other bioelectrical brain signal) characteristic (in the time domainor frequency domain) that indicates patient 12 is in a movement state.While FIG. 14 is described with respect to processor 42 of sensingdevice 14, in other examples, a processor of IMD 32 or IMD 62, or aprocessor of another computing device, such as a trial sensing device ormedical device, may be used to determine the relevant EEG signalcharacteristics. Factors that may affect the relevant EEG signalcharacteristic may include factors such as the age, size, and relativehealth of the patient. The relevant EEG signal characteristic may evenvary for a single patient, depending on fluctuating factors such as thestate of hydration, which may affect the fluid levels within the brainof the patient. Accordingly, it may be desirable in some cases tomeasure the EEG signal of a particular patient over a finite trialperiod of time that may be anywhere for less than one week to one ormore months in order to tune the trending data or threshold values to aparticular patient.

In some cases, it may also be possible for the relevant EEG signalcharacteristic to be the same for two or more patients. In such a case,one or more previously determined EEG signal characteristic may be astarting point for a clinician, who may adapt (or “calibrate” or “tune”)the EEG signal characteristic value (e.g., a threshold amplitude orpower value) to a particular patient. The previously generated EEGsignal characteristic value may be, for example, an average of thresholdvalues for a large number (e.g., hundreds, or even thousands) ofpatients.

Processor 42 of sensing device 14 monitors the EEG signal acquired bysensing module 40 from the relevant region of brain 20 of patient 12(80). Sensing module 40 may acquire the EEG signal substantiallycontinuously or at regular intervals, such as at a frequency of about 1Hz to about 100 Hz. The trial period is preferably long enough tomeasure the EEG signal at different hydration levels and during thecourse of the initiation of different types of patient movements (e.g.,moving an arm or leg, running, walking, and so forth). In addition, theEEG signal for more than one region of brain 20 may also be generated todetermine which region of brain 20 provides the most relevant indicationof the movement state. The region of brain 20 that provides the mostrelevant indication of the movement state may influence where electrodearray 18 is positioned.

During the same trial period of time, an actual movement of patient 12is sensed and recorded (124). Any suitable technique may be used fordetecting the actual movement of the patient, such as using an externalor implanted accelerometer or patient feedback via a patient programmeror another device to indicate patient 12 moved or attempted to move. Ifpossible, patient 12 may, for example, press a button on a device (e.g.,programmer 38 of FIG. 2) prior to, during or after a movement to causethe device to record the date and time, or alternatively, cause sensingdevice 14 to record the date and time of the movement within memory 46.

The time period that begins just prior to the actual movement andcontinues into the actual movement substantially correlates to themovement state of patient 12. Thus, a time period just prior to theactual movement is associated (or correlated) with the measured EEGsignal (126) in order to determine the amplitude of the signal, thepower level of the mu rhythm component of the EEG signal, or other EEGsignal characteristics that are indicative of the movement state. In oneexample, a clinician or computing device may review the data relating tothe actual movement of patient 12, and associate the EEG signal within acertain time range prior to the actual movement, e.g., 1 millisecond(ms) to about 3 seconds, with the actual patient movement. The clinicianor computing device may compare the EEG signals for two or more recordedmovements in order to confirm that the particular EEG signalcharacteristic is indicative of the movement state.

After correlating the EEG signal with a movement, the clinician mayrecord the EEG signal characteristic (128) for later use by processor 42of sensing device 14. Alternatively, a separate computing device mayautomatically determine the relevant EEG signal characteristic. In eachof the examples described above, the relevant EEG signal characteristic,whether in the form of one or more templates or threshold values, may bestored within memory 46 of sensing device 14 or a memory of anotherimplanted or external device, such as a programming device 38 or IMD 32.

In some cases, a clinician or computing device may also correlate aparticular EEG signal with a particular movement or an EEG signal fromwithin a particular region of the motor cortex of brain 20 with aparticular movement. For example, if patient 12 is afflicted with tremorthat affects the patient's arm during arm movement, and gait freeze thataffects both the patient's legs, processor 42 may distinguish between anEEG signal that indicates prospective movement of the patient's arm, andan EEG signal that indicates prospective movement of the patient's legs.Cue generator 54 of external cue device 16 may be configured to deliverdifferent external cues based on the particular movement indicated bythe detected EEG signals.

Any suitable means may be used to correlate particular movement with anEEG signal or an EEG signal within a particular region of the motorcortex. For example, an accelerometer may indicate the movement duringthe trial period or patient 12 may record the type of movement occurringat a particular time, e.g., via a patient programmer or another portableinput mechanism. The clinician or computing device may then associatethe accelerometer outputs or the patient input with EEG signal of one ormore regions of the motor cortex in order to associate a particularmotor cortex region with a particular movement.

Brain activity within DLPF cortex of brain 20 of patient 12 may beindicative of prospective movement, and, therefore, a movement state ofpatient 12. In some examples, a therapy system that is useful forcontrolling a movement disorder may sense brain signals within the DLPFcortex of brain 20 of patient 12 and time the delivery of therapy suchthat the therapy is delivered prior to perception of the movement bypatient 12. This timing of therapy delivery to patient 12 may helpminimize perception of any movement disorder symptoms by patient 12. Insome cases, the therapy system may time the delivery of therapy suchthat patient 12 does not substantially perceive an inability to initiatemovement or another effect of a movement disorder.

The DLPF cortex is an anterior portion of the neocortex of brain 20(i.e., the frontal lobe), and plays a role in early initiation ofexecutive thoughts and actions. The initiation of executive thoughts andactions in the DLPF cortex may occur prior to perception of suchthoughts and actions by patient 12. Thus, bioelectrical signals withinthe DLPF cortex may indicate prospective movement of patient 12 prior tothe generation of bioelectrical signals within the premotor cortex orthe primary motor cortex that indicate movement or intent of movement.Sensing activity in the DLPF cortex of brain 20 may be used to detectearly, premovement signals, which may then be used to control deliveryof a therapy that controls the movement disorder. In this way,electrical activity within the DLPF cortex may be a “biomarker” or“biosignal” that is indicative of prospective movement of patient 12.

In some examples described herein, biosignals within the DLPF cortex ofbrain 20 of patient 12 may be used to detect a movement state of patient12, and detection of the movement state may be used to control theoperation of a device, such as a therapy delivery device or anon-medical device. By continuously or intermittently sensing activitywith the DLPF cortex, prospective movement of patient 12 may be detectedbefore patient 12 moves, or even perceives the movement. In someexamples, upon detecting prospective movement of patient 12, therapydelivery may be triggered or adjusted in order to help patient 12initiate muscle movement or otherwise control any effects of themovement disorder. In some examples, the therapy is delivered to patient12 without any recognized delays by patient 12 (e.g., without anysignificant amount of time between the patient's recognition of a desireand inability to move and the delivery of therapy to help initiatemovement).

The brain signals sensed within the DLPF cortex of brain 20 may providean input to control a therapy delivery device, such as external cuedevice 16 (FIG. 1A) or IMD 62 (FIG. 5). As an example, external sensingdevice 14 may sense brain signals within the DLPF cortex of brain 20 ofpatient 12 with the aid of external electrode array 18 (FIG. 1A).Sensing electrodes 24A-24E (FIG. 1B) may be positioned on an exteriorsurface of patient 12 near the cortex of brain 20. In other examples,implanted sensing device 32 (FIG. 2) may sense brain signals within theDLPF cortex of brain 20 with the aid of implanted electrode array 34.External sensing device 14 and implanted sensing device 32 may generatea control signal or otherwise communicate the sensed brain signal to atherapy delivery device, such as external cue device 16 (FIG. 1) or IMD62 (FIG. 5), which may deliver therapy to patient 12 to help control amovement disorder of patient 12 upon determining that the brain signalis indicative of prospective movement of patient 12. In other examples,external sensing device 14 or implanted sensing device 32 may determinewhether the brain signal within the DLPF cortex indicates a movementstate of patient 12.

FIG. 15 is a conceptual block diagram illustrating an example therapysystem 130 that may be used to deliver therapy to brain 20 of patient 12in response to detecting brain signals within DLPF cortex 132 that areindicative of prospective movement of patient 12. Detection of the brainsignal indicative of prospective movement of patient 12 may beindicative of a movement state of patient 12. Therapy system 130includes IMD 134, which is substantially similar to IMD 62 of FIG. 5. Inaddition to therapy module 70, processor 72, telemetry module 74, memory76, and power source 78, which are described above with respect to FIG.5, IMD 134 includes sensing module 136, which senses bioelectricalsignals within brain 20 of patient 12. In the example shown in FIG. 15,sensing module 136 is electrically coupled to implantable medical lead138, which includes electrodes 140A-140D to sense bioelectrical signalswithin DLPF cortex 132 of brain 20 of patient 12.

The control of modules 70, 74, 76, and 136 may be implemented asprogrammable features, applications or processes of processor 72, orimplemented via other processors or hardware units. Furthermore, thecontrol of modules 70, 74, 76, and 136 may be implemented in hardware,software, and/or firmware, or any combination thereof.

Sensing circuitry within sensing module 136 monitors physiologicalsignals from DLPF cortex 132 of brain 20 via one or more sensingelectrodes 140A-140D under the control of processor 72. Sensing module136 may sense activity within DLPF cortex 132 using sensing levels ofabout 5 microvolts root-means-square (μV rms) to about 200 μV rms. Inthe example shown in FIG. 15, processor 72 includes a signal processorto process the signals from DLPF cortex 132, while in other examples,sensing module 136 may include the signal processor. Processor 72 maysample the signals (either digital or analog) from sensor module 136,and process the sensor signals to determine whether the signals fromDLPF cortex 132 are indicative of prospective movement of patient 12. Ifprospective movement is detected, processor 72 may control therapymodule 70 to initiate or adjust delivery of electrical stimulationtherapy to PPN 48.

Processor 72 may control therapy module 70 to deliver the electricalstimulation signals via selected subsets of electrodes 66A-66D withselected polarities. For example, electrodes 66A-66D may be combined invarious bipolar or multi-polar combinations to deliver stimulationenergy to stimulation sites within brain 20. Processor 72 may alsocontrol therapy module 70 to deliver each stimulation signal accordingto a different program, thereby interleaving programs to simultaneouslytreat different symptoms of the movement disorder, provide a combinedtherapeutic effect, and, in some cases, another condition that may becontrolled or otherwise treated by the stimulation therapy.

In the example shown in FIG. 15, electrodes 66A-66D of lead 64 arepositioned to deliver therapy to PPN 142 of brain 20 of patient 12 inresponse to sensing module 136 sensing a biomarker indicative ofprospective movement of patient 12 within the DLPF cortex 132. In otherexamples, however, stimulation may be delivered to other regions ofbrain 20.

PPN 142 is located in the brainstem of brain 20, caudal to thesubstantia nigra and adjacent to the superior cerebellar penduncle. Thebrainstem is located in the lower part of brain 20, and is adjacent toand substantially continuous with the spinal cord of patient 12. PPN 142is a major brain stem motor area and controls gait and balance ofmovement, as well as muscle tone, rigidity, and posture of patient 12.

It is believed that DLPF cortex 132 of brain 20 controls activity withinPPN 142. DLPF cortex 132 control of PPN activity may be hindered inpatients with Parkinson's disease (PD). Therapy delivery to PPN 142 inresponse to a biomarker detected within DLPF cortex 132 may be used tonormalize the DLPF cortex control of PPN activity. For example, externalsensing electrodes 140A-140D may sense brain activity within DLPF cortex132 and processor 72 may determine whether the brain signals areindicative of prospective movement of patient 12. Upon determining thatthe brain signals within DLPF cortex 132 indicate patient 12 is in amovement state, processor 72 may control therapy module 70 initiate oradjust the delivery of stimulation to PPN 142. In this way,bioelectrical activity within DLPF cortex 132 within a certain rangetriggers delivery of electrical stimulation to PPN 142 by therapy module70, and the therapy delivery may act as a surrogate to normal brainfunction (i.e., the “circuit” between DLPF cortex 132 and PPN 142).

The stimulation administered by IMD 134 to PPN 142 may be selected basedon the specific movement disorder of patient 12, and the effect of thestimulation on other parts of brain 20. For example, stimulation using arelatively high frequency (e.g., greater than 100 Hz) to block theoutput of the pars compacta region of PPN 142 may decrease theexcitatory input to the ventrolateral (VL) thalamus, which may be usefulfor treating hyperkinetic movement disorders. On the other hand,stimulation using a low frequency to facilitate the excitatory output ofthe pars compacta region of PPN 142 may alleviate symptoms for personswith hypokinetic movement disorders. Glutamatergic neurons within thepars dissipatus region of PPN 142 receive outputs from the mainsubthalamic nucleus, the internal globus pallidus, and the substantianigra pars reticulate, and provide the main outflow of information tothe spinal cord. Stimulation to influence glutamatergic neurons withinthe pars dissipatus region of PPN 142 may be useful to initiate orotherwise control patient movement. The stimulation parameter values mayvary depending upon the type of neurons in PPN 142 that are stimulated.Continuous mid-frequency stimulation on the order of about 20 Hz toabout 60 Hz may be useful for initiating patient movement, whilerelatively high frequency stimulation (e.g., greater than about 100 Hz)may be useful for achieving other effects, such as reducing musclerigidity.

Providing stimulation on demand, when movement-specific activation isdesired, may be more beneficial than providing continuous orsubstantially continuous stimulation to PPN 142 or other brain sites. Insome cases, continuous or substantially continuous delivery ofstimulation to PPN 142 may interfere with other brain 20 functions, suchas activity within subthalamic nucleus, as well as therapeutic deepbrain stimulation in other basal ganglia sites. In addition providingstimulation intermittently or upon the sensing of movement by patient 12(or in the case of DLPF cortex sensing, the thought that precedes actualmovement), may be a more efficient use of energy. Stimulation may not benecessary when, for example, patient 12 is not moving or thinking aboutmovement. Delivering stimulation only when needed or when desirable mayhelp conserve a power source within IMD 134. As previously described,delivering stimulation or another therapy to patient 12 on demand, e.g.,when patient 12 is initiating movement or thinking about initiatingmovement, may help minimize the patient's adaptation to the therapy.

IMD 134 may also be configured to deliver stimulation to other regionswithin patient 12, in addition to or as an alternative to deliveringstimulation to PPN 142. As examples, IMD 134 may deliver electricalstimulation therapy to the thalamus, basal ganglia structures (e.g.,globus pallidus, substantia nigra, subthalamic nucleus), zona inserta,fiber tracts, lenticular fasciculus (and branches thereof), ansalenticularis, and/or the Field of Forel (thalamic fasciculus) of brain,or to the spinal cord of patient 12, nerves, muscles or muscle groups ofpatient 12, or another suitable site within patient 12 in order to helppatient 12 control muscle movement.

In addition to or instead of utilizing activity within DLPF cortex 132as an input to control delivery of electrical stimulation or fluids(e.g., drugs), activity within DLPF cortex 132 may be useful foractivating or adjust other forms of therapy, such as the delivery of asensory cue (e.g., visual, auditory or somatosensory cue) with animplanted device or an external device. For example, upon detecting asignal within DLPF cortex 132 that is indicative of prospective movementof patient 12, processor 72 may control therapy module 70 to stimulate avisual cortex of brain 20 simulate a visual cue or deliver an internalsound in order to simulate a particular sight or sound that activatespatient movement. As The particular sensory cue may differ betweenpatients and between patient conditions. Somatosensory cutes may includea vibration or another tactile cue. Alternatively, the sensory cue maybe delivered via another implanted device. As another example, processor72 may control an external cue device 16 (FIG. 1A) to deliver the visualcue in response to detecting a movement state of patient 12 based onbrain signals within DLPF cortex 132 of brain 20. An implanted sensorycue device may be more discreet than external device, but an externaldevice may be less invasive. Other sensory cue delivery techniques arecontemplated.

In some cases, if patient 12 is afflicted with Parkinson's disease,patient 12 may be susceptible to gait freeze, which may be anincapacitating symptom of Parkinson's disease. In some patients, asensory cue may help activate PPN 142 or another portion of brain 20that is responsible for activating movement. Delivering a sensory cue inresponse to detection of a brain signal within DLPF cortex 132 that isindicative of prospective patient movement may disrupt brain activitythat is hindering patient movement, thereby enabling patient 12 toinitiate movement. The sensory cue may be used in addition to or insteadof electrical stimulation therapy or fluid delivery therapy.

In addition to controlling therapy delivery, sensing activity withinDLPF cortex 132 may be useful for controlling other devices becauseactivity within the DLPF cortex 132 may generally be a biological markerfor movement. Detection of movement may be useful for activating otherdevices, such as the activation of a prosthetic limb, activation of apatient transport device (e.g., a wheelchair), and so forth.

In other examples, the activity detected within DLPF cortex 132 may beused in a therapy system to control other types of therapy. For example,fluid (e.g., a drug) may be delivered to one or more regions of brain20, the spinal cord, muscle, muscle group or another site withinpatients 14 in order to help patient 12 initiate muscle movement. Asanother example, a sensory cue may be delivered to patient via anexternal device or an implanted device to help patient 12 initiatemuscle movement. In general, the delivery of electrical stimulation ordrug therapy may help alleviate, and in some cases, eliminate symptomsassociated with movement disorders.

Although FIG. 15 illustrates an example in which therapy module 70 andsensing module 136 are disposed in a common housing, the disclosure isnot limited to such examples. In other examples, therapy module 70 maybe separate from sensing module 136. For example, therapy module 70 maybe configured to deliver therapy to a region of patient 12 other thanbrain 20. As examples, sensing module 136 or another sensing module maydetect signals within DLPF cortex 132 that are indicative of prospectivemovement of patient 12 and initiate functional electrical stimulation(FES) or transcutaneous electrical stimulation (TENS) of a muscle ormuscle group of patient 12 in order to help initiate movement or helppatient 12 control movement of a limb or other body part. In the case ofFES, IMD 134 may be implanted to deliver stimulation to a muscle, ratherthan brain 20 of patient 12.

As another example, sensing module 136 may be incorporated into anexternal sensing device that is coupled to an external sensor, such asexternal sensing device 14 shown in FIG. 1A. In some examples, externalsensing device 14 (FIG. 5) may monitor a brain signal within DLPF cortex132 of brain 20 via external sensing electrodes 24A-24E and processesthe signals to determine if the signals are indicative of prospectivemovement of patient 12. Upon detecting a signal indicative ofprospective movement, sensing device 14 may provide an input to IMD 62(FIG. 5) via wireless telemetry, such as with RF communicationtechniques. In response to receiving the input from sensing device 14,processor 72 of IMD 134 may control delivery of therapy to patient 12,such as initiating the delivery of electrical stimulation to patient viatherapy module 70, or adjusting parameters of the stimulation.

FIG. 16 illustrates the therapy delivery system shown in FIG. 15, inwhich IMD 134 includes both therapy module 70 to provide electricalstimulation therapy via electrodes 66A-66D (collectively “electrodes66”) and sensing module 136 to sense activity in DLPF cortex 132 viaimplanted sensing electrodes 140A-140D (collectively “electrodes 140”).In the example shown in FIG. 16, leads 64, 138 are coupled to a commonlead extension 144. In other examples, however, at least one of theleads 64, 138 may be directed coupled to IMD 134 without the aid of leadextension 144. In FIG. 16, instead of or in addition to controllingtherapy module 70 upon sensing a particular level of activity withinDLPF cortex 132, processor 72 (or another controller within IMD 134) isconfigured to control external device 146.

FIG. 16 also illustrates external device 146, which may be any devicethat is not fully-implanted within patient 12. Processor 72 maycommunicate with external device 146 via telemetry module 74 of IMD 134(FIG. 15) according to any suitable wireless communication techniqueknown in the art, such as RF communication techniques. External device146 and IMD 134 may be configured for unidirectional communication(i.e., one-way communication from IMD 134 to external device 146) orbidirectional communication. Thus, in some examples, external device 146may include a transceiver for sending data to and receiving data fromIMD 134, or merely a receiver configured to receive data (e.g., commandsor instructions) from processor 72 of IMD 134.

Examples of external devices 146 that processor 72 may control include adevice mounted to patient 12 to provide a sensory cue to help initiatepatient movement, a prosthetic limb, a patient transport device, or evennon-medically related devices, such as appliances (e.g., light source,stove, television, radio, computing device, semi-automated doors, and soforth). An “appliance” generally refers to a device that is used for aparticular use or purpose unrelated to therapy delivery to patient 12.

In general, brain signals detected within DLPF cortex 132 may be used tocontrol external device 146, regardless of whether device 146 isconfigured to treat or otherwise control a movement disorder or whetherdevice 146 is unrelated to the movement disorder. IMD 134 or anothersensing device may monitor signals within DLPF cortex 132 of brain 20 ofpatient 12 and upon detecting a signal indicative of prospective patientmovement, processor 72 may send a control signal to external device 146via telemetry module 74.

In examples in which external device 146 includes a prosthetic limb orthe like, external device 146 may include a power source to providepower to a sensing circuitry to sense movement of a particular muscle ormuscle group of patient 12 or other components. The prosthetic limb mayinclude a sleep mode during periods of disuse of the prosthetic in orderto conserve power. Upon detecting the early signs that patient 12 wantsto initiate movement (via a signal within DLPF cortex 132), IMD 134 maysend a signal to external device 146 in order to wake the prosthetic upfrom its sleep state. If external device 146 includes a patienttransport device, such as a wheelchair, the wheelchair may be configuredto activate or propel in a particular direction based on detection of asignal within DLPF cortex 132 that indicates patient 12 is executingthoughts of movement. In examples in which external device 146 includesa lamp or another nonmedical appliance, IMD 134 may send a signal to areceiver on the lamp to turn the lamp on if IMD 134 detects a signalindicative of prospective movement of patient 12.

In other examples, an external device, such as external sensing device14 (FIG. 1A) may be used to sense brain signals within DLPF cortex 132of brain 20 and control external device 146 upon determining patient 12is in a movement stated based on the sensed brain signals. Moreover, insome examples, external device 146 may be controlled by an external oran implanted medical device that does not include therapy module 70 todeliver therapy to patient 12.

FIG. 17 is a flow diagram of an example technique for controlling adevice, such as an external device 146 (FIG. 16) or therapy delivery topatient 12, e.g., via therapy module 70 within IMD 134 (FIG. 15). Whilethe techniques shown in FIGS. 17-19 and 21 are primarily described asbeing performed by processor 72 of IMD 134, in other examples, aprocessor of another device, such as external sensing device 14,external cue device 16 or implanted sensing device 32, may perform anypart of the techniques described herein.

Processor 72 may monitor brain signals within DLPF cortex 132 (FIG. 15)substantially continuously or at regular intervals (148). In examples inwhich a therapy system includes implanted sense electrodes 140 (FIG.15), sensing electrodes 140 may be positioned proximate to DLPF cortex132. In examples in which a therapy system includes external sensingelectrodes 24A-24E (FIG. 1B), sensing electrodes 24A-24E may bepositioned on a surface of patient 12 proximate to DLPF cortex 132. Insome cases, implanted sensing electrodes 140 may monitor the activity ofDLPF cortex 132 better than external sensing electrodes 24A-24E due tothe proximity to DLPF cortex 132. In either case sensing module 136(FIG. 15) of IMD 134 may receive signals from sensing electrodes 24A-24Eor 140 that are indicative of the bioelectrical activity within DLPFcortex 132.

Processor 72 of IMD 134 may receive sensor signals from sensing module136 and process the sensor signals to determine whether the sensorsignals indicate a prospective movement of patient 12 (150). If thesensor signals are not indicative of prospective movement, sensingmodule 136 may continue sensing activity within DLPF cortex 132 underthe control of processor 72 (148). If the sensor signals are indicativeof prospective movement, processor 72 may control of a device (152). Aspreviously discussed, the device may be external device 146 (FIG. 16) ora therapy delivery device (e.g., external cue device 16 or therapymodule 70). For example, upon processing the DLPF cortex signalsreceived from sensing module 136 and determining that patient 12 isinitiating thoughts of movement, processor 72 may activate therapymodule 70 of IMD 132. Therapy module 70 may activate therapy in responseto detection of prospective movement of patient 12 via the signals fromDLPF cortex 132 or adjust therapy (e.g., increase therapy in order tohelp patient 12 initiate muscle movement).

A signal processor within processor 72 or sensing module 136 of IMD 134may determine whether the DLPF cortex signals sensed by sensing module136 are indicative of prospective movement using any suitable technique.As various examples, the electrical signals may be analyzed foramplitude, temporal correlation or frequency correlation with a templatesignal, or combinations thereof. For example, the instantaneous oraverage amplitude of the electrical signal over a period of time may becompared to an amplitude threshold. As another example, a slope of theamplitude of the signal over time or timing between inflection points orother critical points in the pattern of the amplitude of the electricalsignal over time may be compared to trend information. A correlationbetween the inflection points in the amplitude waveform of theelectrical signal or other critical points and a template may indicateprospective movement. Sensing module 136 or processor 72 may conditionthe signals, if necessary.

As another example, the signal processor within processor 72 or sensingmodule 136 may perform temporal correlation by sampling the waveformgenerated by the electrical signals within DLPF cortex 132 with asliding window and comparing the waveform with a stored templatewaveform. For example, processor 72 of IMD 134 or a processor of anotherdevice, implanted or external, may perform a correlation analysis bymoving a window along a digitized plot of the amplitude waveform of DLPFcortex 132 signals at regular intervals, such as between about onemillisecond to about ten millisecond intervals, to define a sample ofthe brain signal. The sample window may be slid along the plot until acorrelation is detected between the waveform of the template and thewaveform of the sample of the brain signal defined by the window. Bymoving the window at regular time intervals, multiple sample periods aredefined. The correlation may be detected by, for example, matchingmultiple points between the template waveform and the waveform of theplot of the brain signal over time, or by applying any suitablemathematical correlation algorithm between the sample in the samplingwindow and a corresponding set of samples stored in the templatewaveform.

Frequency correlation is described in further detail below. In general,in some examples, the brain signal from DLPF cortex 132 may be analyzedin the frequency domain to compare selected frequency components of anamplitude waveform of the signal within DLPF cortex 132 to correspondingfrequency components of a template signal. For example, one or morespecific frequency bands may be more revealing of prospective movementof patient 12 than others, and the correlation analysis may include aspectral analysis of the electrical signal component in the revealingfrequency bands. The frequency component of the electrical signal may becompared to a frequency component of a template.

FIG. 18A is a flow diagram illustrating an example technique foranalyzing electrical activity within DLPF cortex 132 to determinewhether the activity indicates prospective patient movement. Processor72 of IMD 134 may implement the technique shown in FIG. 18A in order toprocess electrical signals sensed within DLPF cortex 132 and predictmovement of patient 12. Sensing module 136 of IMD 134 may substantiallycontinuously or intermittently monitors electrical activity within DLPFcortex 132 via one or more electrodes 140 (148). Processor 72 maycompare an amplitude of the monitored electrical signal to apredetermined threshold value (154). The relevant amplitude may be, forexample, the instantaneous amplitude of an incoming electrical signal oran average amplitude of electrical signal over period of time. In oneexample, which is described below with reference to FIG. 18B, thethreshold value is determined during the trial phase that precedes theimplementation of a chronic therapy delivery device within patient 12.

If the measured electrical signal from within DLPF cortex 132 is greaterthan the threshold value (156), processor 72 may control the operationof a device based on the brain signal within the (152). In one example,if the measured electrical signal from within DLPF cortex 132 is greaterthan the threshold value (156), processor 72 may control therapy module70 of IMD 132 to initiate therapy delivery or adjust at least onetherapy parameter value. Processor 72 may adjust a therapy parametervalue by switching therapy programs that defines the therapy parametervalues for therapy module 70 or by modifying one or more therapyparameter values. A clinician may limit the extent to which processor 72may adjust a therapy parameter value, such as, for example, by setting aminimum and maximum value for various therapy parameter values (e.g.,stimulation amplitude or frequency or a frequency of a delivery of adrug bolus). In other examples, therapy may be delivered to patient 12via a therapy delivery device that is separate from sensing module 136.In another example, processor 72 may control external device 146 todeliver a sensory cue or perform another function (e.g., turn a light onor propel a patient transport device). In addition, other actions may betriggered if the amplitude of the electrical signal within DLPF cortex132 exceeds or equals the amplitude threshold. For example, IMD 132,external device 146 or another device, such as an external programmingdevice may also record the electrical signals for later analysis by aclinician. On the other hand, if the amplitude of the electrical signalis less than or equal to the threshold value (156), processor 72 maycontinue monitoring the electrical activity within DLPF cortex 132.

After processor 72 controls a device in response to receiving the signalwithin DLPF cortex 132 that is indicative of prospective movement (152),sensing module 136 may continue measuring the electrical activity withinDLPF cortex 132 (148). This pattern of responsive therapy delivery maycontinue indefinitely. Alternatively, the electrical activity may bemeasured for a limited period of time, such as periodically duringrelevant times during the day (e.g., when the patient is awake).

FIG. 18B is a flow diagram illustrating an example technique fordetermining one or more threshold amplitude values that suggest a sensedsignal from within DLPF cortex 132 is indicative of prospective movementof patient 12. The absolute amplitude value that indicates intendedmovement may differ depending on the patient. Factors that may affectthe threshold value may include factors such as the age, size, andrelative health of the patient. The relevant threshold amplitude mayeven vary for a single patient, depending on fluctuating factors such asthe state of hydration, which may affect the fluid levels within thebrain of the patient. Accordingly, it may be desirable in some cases tomeasure the DLPF cortex 132 activity of a particular patient over afinite trial period of time that may be anywhere for less than one weekto one or more months in order to tune the trending data or thresholdvalues to a particular patient.

It may be possible for the relevant threshold values to be the same fortwo or more patients. In such a case, one or more previously generatedthreshold values may be a starting point for a clinician, who may adapt(or “calibrate” or “tune”) the threshold values to a particular patient.The previously generated threshold values may be, for example, anaverage of threshold values for a large number (e.g., hundreds, or eventhousands) of patients.

The electrical activity within DLPF cortex 132 is monitored during atrial period of time with IMD 134 (FIG. 15), although an externalsensing device 14 or another type of sensing device may also be used inother examples (157). Sensing module 136 of IMD 134 may measure theamplitude of the electrical activity substantially continuously or atregular intervals, such as at a frequency of about 1 Hz to about 100 Hz.The trial period is preferably long enough to measure the amplitude ofthe electrical signal within DLPF cortex 132 at different hydrationlevels and during the course of the initiation of different patientmovements (e.g., moving an arm or leg, running, walking, and so forth).During the same trial period of time, actual movement of patient 12 issensed and recorded (158). Any suitable technique may be used fordetecting the actual movement of the patient, such as using an externalor implanted accelerometer or patient input via a patient programmer oranother device to indicate patient 12 moved or attempted to move. Ifpossible, patient 12 may, for example, press a button on a device priorto, during or after a movement to cause the device to record the dateand time, or alternatively, cause the trial device or a programmingdevice to record the date and time of the movement.

The time period prior to the movement, which substantially correlates tothe time period in which patient 12 initiated thoughts of movementwithin DLPF cortex 132, are associated (or correlated) with measuredDLPF cortex 132 signals (160) in order to determine the amplitude of thesignal or other threshold values that are indicative prospectivemovement. In one example, a clinician or computing device may review thedata relating to the actual movement of patient 12, and associate theelectrical activity measurements taken within a certain time range priorto the actual movement, e.g., 1 millisecond (ms) to about 3 seconds,with the actual patient movement. The clinician or computing device mayreview the relevant amplitude values for two or more recorded movementsin order to confirm that the threshold value is indicative ofprospective movement.

After correlating the electrical activity with an actual movement, theclinician may record the amplitude of the relevant electrical brainsignal(s) as the relevant threshold value (162). Alternatively, thecorrelation and threshold value recording may be automatically performedby a computing device or with the aid of a computing device. In each ofthe examples described above, the one or more templates may be storedwithin memory 76 (FIG. 15) of IMD 134 or a memory of another implantedor external device, such as a programming device for IMD 134.

In some cases, a clinician or computing device may also correlate aparticular signal within DLPF cortex 132 with a particular movement. Forexample, if patient 12 is afflicted with tremor that affects thepatient's arm during arm movement, and gait freeze that affects both thepatient's legs, processor 72 may distinguish between a DLPF cortex 132signal that indicates prospective movement of the patient's arm, and aDLPF cortex 132 signal that indicates prospective movement of thepatient's legs. Therapy module 136 may then be configured to delivertherapy to PPN 142 (FIG. 15) or different parts of brain 20 or thepatient's body based on the particular movement indicated by signalssensed within DLPF cortex 132.

Any suitable means may be used to correlate particular movement with aDLPF cortex signal. For example, an accelerometer may indicate themovement during the trial period or patient 12 may record the type ofmovement occurring at a particular time, e.g., via a patient programmeror another portable input mechanism. The clinician or computing devicemay then associate the accelerometer outputs or the patient input withDLPF cortex signals in order to determine what types of prospectivemovements the DLPF cortex signals indicate.

FIG. 19A is a flow diagram illustrating an example technique fordetermining whether electrical signals within DLPF cortex 132 areindicative of prospective movement of patient 12. The method shown inFIG. 19A is similar to that shown in FIG. 18A, except that rather thancomparing an amplitude of the measured electrical signal from withinDLPF cortex 132 to a threshold value (154) and controlling a device ifthe amplitude of the measured signal is greater than or equal to thethreshold value (156, 152), the technique shown in FIG. 19A involvesmonitoring a pattern (also referred to as a trend) in the amplitude ofthe measured electrical signal (164). In this way, the method may usesignal analysis techniques, such as correlation, to monitor forprospective patient movement and implement a closed-looped system forcontrolling a device. In other examples, a trend in a signalcharacteristic other than the amplitude may be compared to a template.

Processor 72 may perform temporal correlation with a template bysampling the DLPF cortex signal with a sliding window and comparing thesampled waveform with a stored template waveform. For example, processor72 may perform a correlation analysis by moving a window along adigitized plot of the amplitude waveform of a DLPF cortex signal atregular intervals, such as between about one millisecond to about tenmillisecond intervals, to define a sample of the DLPF cortex signal. Thesample window may be slid along the plot until a correlation is detectedbetween the waveform of the template and the waveform of the sample ofthe DLPF cortex signal defined by the window. By moving the window atregular time intervals, multiple sample periods are defined. Thecorrelation may be detected by, for example, matching multiple pointsbetween the template waveform and the waveform of the plot of the DLPFcortex signal over time, or by applying any suitable mathematicalcorrelation algorithm between the sample in the sampling window and acorresponding set of samples stored in the template waveform.

If the pattern in the signal amplitude over time substantially matches apattern template (166), processor 72 controls a device, e.g., initiatesor adjusts therapy delivery to patient 12 to help patient 12 initiatemovement or maintain movement (152). In some examples, the templatematching algorithm that is employed to determine whether the patternmatches the template (166) may not require a one hundred percent (100%)correlation match, but rather may only match some percentage of thepattern. For example, if the amplitude waveform of the DLPF cortexsignal exhibits a pattern that matches about 75% or more of thetemplate, the algorithm employed by processor 72 within IMD 134 orexternal sensing device 14 may determine that there is a substantialmatch between the pattern and the template.

In one example, a pattern in the amplitude of the electrical signalsfrom DLPF cortex 132 that is indicative prospective movement mayrepresent a rate of change of the amplitude measurements over time. Forexample, a positive increase in the time rate of change (i.e., the slopeor first derivative) of the signal amplitude may indicate patient 12 isintending to move. Accordingly, if the time rate of change of theamplitude measurements matches or exceeds the time rate of change of thetemplate, processor 72 may control therapy module 36, external device146 or another device (152). The exact trend that is indicative ofprospective movement may be determined during a trial phase, which isdescribed below with reference to FIG. 19B.

FIG. 19B is a flow diagram illustrating an example technique fordetermining one or more trend templates for determining whether a signalmeasured within DLPF cortex 132 is indicative of prospective patientmovement. The technique shown in FIG. 19B is similar that shown in FIG.18B, except that rather than associating actual movement with a specificamplitude value or range of amplitude values, a pattern of acharacteristic of the signal is associated with an actual movement inorder to generate a template. As with the threshold values, the trend inthe amplitude or other characteristic that indicates prospectivemovement may differ depending on the patient. It is also believed thatit is possible for the relevant trending data to be the same for two ormore patients. In such a case, one or more previously generated trendtemplates may be a starting point for a clinician, who may adapt (or“calibrate” or “tune”) the template to a particular patient.

In the technique shown in FIG. 19B, after correlating (or associating)an actual movement or actual attempted movement with electrical signalsthat were sensed within DLPF cortex 132 prior to the movement orattempted movement (160), a pattern in signals may be determined (168).As previously discussed, the trend may be a rate of change, i.e., slope,of the amplitude of the measured signals over time or a series ofdifferent slopes and transition points in an amplitude waveform.

A pattern may be determined (168) by any suitable means. In one example,the clinician may plot the amplitude of the relevant electrical signalover time and use the slope of the plot in the slope as the trendtemplate. The clinician may review the amplitude waveforms associatedwith two or more recorded movements in order to confirm that thetemplate is indicative of prospective movement. Alternatively, acomputing device may generate the plot. In other examples, a pattern ortrend other than a simple slope of the impedance measurements over timemay define the template. For example, the template may include a seriesof different slopes and transition points in the amplitude waveform ofthe measured DLPF cortex signal. After determining the relevant trend inthe electrical signals that indicates prospective patient movement, theclinician and/or computing device may record the trend in memory 76(FIG. 15) of IMD 134 or another device, and the trend may define atemplate for determining prospective movements in the future.

While processing any electrical activity within DLPF cortex 132 may beuseful, focusing on a specific frequency band of the sensed electricalactivity may also yield useful information, and in some cases, moreuseful information. For example, frequency components of the electricalactivity waveform of may be analyzed and compared to frequencycomponents of a template waveform. As previously described, differentfrequency bands are associated with different activity in brain 20. Somefrequency ranges may be more revealing of prospective patient movementthan other frequency ranges. This concept may be applied to determiningwhether activity within the DLPF cortex 132 indicates the early signs ofmovement (i.e., prospective movement).

For example, sensing module 136 may monitor the electrical activitywithin DLPF cortex 132, and either sensing module 136 or processor 72may tune the electrical data to a particular frequency in order todetect the power level (also referred to as the “energy” or anindication of the signal strength) within a low frequency band (e.g.,the alpha or delta frequency band from Table 1), the power level with ahigh frequency band (e.g., the beta or high gamma frequency bands inTable 1) or both the power within the low and high frequency bands. Thepower level within the selected frequency band may be indicative ofwhether the DLPF cortex 132 activity is indicative of prospectivepatient movement. In some cases, the high gamma band component of theDLPF cortex 132 bioelectrical signal may be easier to extract than thealpha band component because the gamma band includes less noise than thealpha band. The noise may be due to, for example, other bioelectricalsignals (e.g., an ECG signal).

FIG. 20 illustrates a conceptual spectrogram 172 of the alpha (α) bandcomponents of an EEG plot generated with external surface electrodesmonitoring electrical activity within an occipital cortex of a brain ofa human subject. Strong signals within the relatively low frequencyband, i.e., the alpha band, as shown in regions 174 indicate that theactivity within the alpha frequency band is high, and accordingly, thehuman subject is probably not executing thoughts of movement. Thespectrogram shown in FIG. 20 may occur when, for example, a patient issitting, sleeping, or otherwise stagnant. Strong signals falling withina relatively low frequency band, as shown via the spectrogram shown inFIG. 20, are not indicative of prospective movement.

Based on this relationship between the strength of the bioelectricalsignals within brain 20 and a signal strength within a relatively lowfrequency band, in some examples, processor 72 of IMD 134 may determinewhether the signal strength of the electrical activity sensed withinDLPF cortex 132 within a relatively low frequency band is relatively lowin order to determine whether the biosignal sensed within DLPF cortex132 is indicative of prospective patient movement. A technique similarto the technique shown in FIG. 8 may be used by processor 72. Asdescribed above, FIG. 8 illustrates a technique for determining whetheran EEG signal indicates patient 12 is in a movement state.

In some examples, a signal processor, e.g., within processor 72 of IMD134, may analyze the strength of the monitored DLPF cortex signal withina relatively low frequency band (e.g., the alpha or delta frequencybands from Table 1 above). If the power level within the low frequencyband is relatively low, the brain signals may indicate that the powerlevel is ramping up to a higher frequency band (e.g., the beta or highgamma frequency bands from Table 1 above) and patient 12 is initiatingthoughts of movement. Thus, a brain signal sensed within DLPF cortex 132that includes a relatively low power level within a low frequency bandmay be indicative of prospective movement. Alternatively, processor 72may determine whether the power within the low frequency band increasedor decreased relatively quickly over time. A decrease in the power inthe low frequency band may indicate patient 12 wants to initiate motionbecause the power level is ramping up to a higher frequency band, whichis associated with prospective movement.

In response to detecting the signal indicative of prospective movementbased on the power of the brain signal sensed within DLPF cortex 132within one or more frequency bands, processor 72 may control a device,which may include therapy module 70 (FIG. 15) and/or control an externaldevice 146 (FIG. 16). On the other hand, if the power of the brainsignal sensed within DLPF cortex 132 in the lower frequency band isrelatively high, patient 12 may not be initiating thoughts of movement,and processor 72 may not take any action, while sensing module 136 ofIMD 134 may continue monitoring the activity within DLPF cortex 132.

In other examples, sensing module 136 may monitor the power level withina high frequency band (e.g., gamma or beta bands), and an increasedpower level in the high frequency band may indicate patient 12 is in theearly stages of movement (e.g., patient 12 is thinking about moving).That is, a higher power level in a relatively high frequency band (e.g.,beta or gamma frequency bands) may be a brain signal indicative ofprospective movement of patient 12. In general, if the strong signalsfall within a high frequency band (e.g., the beta or gamma bands fromTable 1), or otherwise do not fall within the lower frequency band,therapy may be activated or adjusted to help patient 12 initiate and/ormaintain movement. In some cases, sensing module 136 may monitor thepower level within both the low and high frequency bands.

In each of the described examples in which processor 72 of IMD 134controls therapy module 70 or external device 146 to initiate therapydelivery to patient 12 in response to detecting prospective movement ofpatient 12, the therapy delivery may be initiated for a predeterminedamount of time or until processor 72 receives an indication that patient12 has stopped moving. In the case of a movement disorder, it may beuseful to deliver therapy to patient 12 for a defined period of time,rather than substantially continuously, in order to help patient 12initiate movement, while conserving power source 78 of IMD 134 (FIG.15).

Similarly, in examples in which processor 72 controls therapy module 70or external device 146 to adjust therapy delivery in response todetecting prospective movement of patient 12 based on brain signalssensed within DLPF cortex 132, therapy module 70 or external device 146may continue delivering therapy at an adjusted level for a predeterminedamount of time or until processor 72 receives an indication that patient12 has initiated movement or stopped moving. The predetermined amount oftime may be selected to be sufficient to initiate patient movement orotherwise control a movement disorder. For example, if initiation ofpatient movement is desired (e.g., to treat hypokinesia), thepredetermined amount of time may be relatively short (e.g., less thanfive seconds). As another example, if functional electrical stimulationto help increase an execution of a movement (e.g., to treatbradykinesia) is desired, the predetermined amount of time may berelatively long (e.g., on the duration of minutes).

As previously indicated, an indication that patient 12 has initiatedmovement or stopped moving may be generated any suitable way. Forexample, processor 72 may receive a signal from a motion sensor (e.g.,an accelerometer) that is placed to detect actual movement of patient12, and, accordingly, may detect the cessation of movement. The motionsensor may be, for example, integrated with IMD 134, external device146, implanted within an arm or leg of patient 12 or otherwise carriedexternally (e.g., on a belt or arm band). The brain signals detectedwithin DLPF cortex 132 may be used to make relatively fast andresponsive adjustments to therapy, whereas the motion sensor may also beused to make longer term adjustments to therapy based, as describedabove with respect to FIG. 12.

As another example, processor 72 may analyze a brain signal from withinDLPF cortex 132 to determine if patient 12 is no longer executingthoughts of movement. For example, if the amplitude of the signal fallsbelow an amplitude threshold or longer matches a template, the brainsignal may indicate patient 12 is no longer executing thoughts ofmovement. In other examples, the ratio of the power level in particularfrequency bands (e.g., delta/gamma) may be compared to a threshold valueto determine whether a brain signal from within DLPF cortex 132indicates prospective movement of patient 12.

In another example, the correlation of changes of power between two ormore frequency bands may be compared to a stored value to determinewhether the signal from DLPF cortex 132 indicates patient 12 is in amovement state or in a rest state (i.e., not in the movement state). Forexample, if the power level within the alpha band decreases andindicates prospective movement of patient 12, and within a certainamount of time or at substantially the same time, the power level withinthe high gamma band of the DLPF cortex signal increases, processor 72may confirm that patient 12 is intending on moving. This correlation ofchanges in power of different frequency bands may be implemented into analgorithm that helps processor 72 eliminate false positives of theprospective movement detection, i.e., by providing confirmation that thelow power level (e.g., as compared to a stored value or trend template)within the alpha band or high power level within the gamma band (e.g.,as compared to a stored value or trend template) indicates prospectivemovement state.

FIG. 21 illustrates an example technique that processor 72 may implementto deactivate or adjust therapy after initiating or adjusting therapy inresponse to detecting a brain signal within DLPF cortex 132 that isindicative of prospective movement. Sensing module 136 of IMD 134 maymonitor activity with DLPF cortex 132 (148) and processor 72 may receivethe DLPF cortex signal from sensing module 136 and analyze a strength ofthe signal within a relatively low frequency band (180), such as analpha band. Rather than determining whether the power level within thelower frequency band is low, processor 72 may determine whether thepower within the relatively low frequency band is high (182), which mayindicate that patient 12 is no longer moving or initiating thoughts ofmovement. If the power level within the low frequency band is high(180), processor 72 may adjust therapy (184), such as by terminatingtherapy or decreasing the intensity of therapy by adjusting one or moretherapy parameter values. Alternatively, processor 72 may focus on thepower level within a high frequency band, and a relatively low powerlevel in the high frequency band may indicate a lack of prospectivemovement.

FIG. 22 is a block diagram illustrating an exemplary frequency selectivesignal monitor 270 that includes a chopper-stabilized superheterodyneinstrumentation amplifier 272 and a signal analysis unit 273. Amplifier272 is described in further detail in commonly-assigned U.S. ProvisionalApplication No. 60/975,372 by Denison et al., entitled “FREQUENCYSELECTIVE MONITORING OF PHYSIOLOGICAL SIGNALS,” and filed on Sep. 26,2007, commonly-assigned U.S. Provisional Application No. 61/025,503 byDenison et al., entitled “FREQUENCY SELECTIVE MONITORING OFPHYSIOLOGICAL SIGNALS, and filed on Feb. 1, 2008, and commonly-assignedU.S. Provisional Application No. 61/083,381, entitled, “FREQUENCYSELECTIVE EEG SENSING CIRCUITRY,” and filed on Jul. 24, 2008. The entirecontents of above-identified U.S. Provisional Application Nos.60/975,372, 61/025,503, and 61/083,381 are incorporated herein byreference. Amplifier 272 is also described in further detail incommonly-assigned U.S. patent application Ser. No. 12/237,868 by Denisonet al., entitled, “FREQUENCY SELECTIVE MONITORING OF PHYSIOLOGICALSIGNALS” and filed on Sep. 25, 2008. U.S. patent application Ser. No.12/237,868 by Denison et al., which published as U.S. Patent ApplicationPublication No. 2009/0082691 on Mar. 26, 2009 is incorporated herein byreference in its entirety.

Signal monitor 270 may utilize a heterodyning, chopper-stabilizedamplifier architecture to convert a selected frequency band of aphysiological signal to a baseband for analysis. The physiologicalsignal may be analyzed in one or more selected frequency bands totrigger therapy delivery to patient 12, trigger adjustment to therapydelivery, and/or trigger recording of diagnostic information. In somecases, signal monitor 270 may be utilized within a separate sensor thatcommunicates with a medical device. For example, signal monitor 270 maybe utilized within sensing device 14 positioned external to patient 12and coupled to external cue device 16 (FIG. 1A). In other examples,signal monitor 270 may be included within an external or implantedmedical device, such as IMD 62 (FIG. 5) or sensing module 136 of IMD 134(FIG. 15).

In general, frequency selective signal monitor 270 provides aphysiological signal monitoring device comprising a physiologicalsensing element that receives a physiological signal, an instrumentationamplifier 272 comprising a modulator 282 that modulates the signal at afirst frequency, an amplifier that amplifies the modulated signal, and ademodulator 288 that demodulates the amplified signal at a secondfrequency different from the first frequency. A signal analysis unit 273that analyzes a characteristic of the signal in the selected frequencyband. The second frequency is selected such that the demodulatorsubstantially centers a selected frequency band of the signal at abaseband.

The signal analysis unit 273 may comprise a lowpass filter 274 thatfilters the demodulated signal to extract the selected frequency band ofthe signal at the baseband. The second frequency may differ from thefirst frequency by an offset that is approximately equal to a centerfrequency of the selected frequency band. In one example, thephysiological signal is an EEG signal and the selected frequency band isone of an alpha, beta or gamma frequency band of the EEG signal. Thecharacteristic of the demodulated signal is power fluctuation of thesignal in the selected frequency band. The signal analysis unit 273 maygenerate a signal triggering at least one of control of therapy to thepatient or recording of diagnostic information when the powerfluctuation exceeds a threshold.

In some examples, the selected frequency band comprises a first selectedfrequency band and the characteristic comprises a first power. Thedemodulator 288 demodulates the amplified signal at a third frequencydifferent from the first and second frequencies. The third frequencybeing selected such that the demodulator 288 substantially centers asecond selected frequency band of the signal at a baseband. The signalanalysis unit 273 analyzes a second power of the signal in the secondselected frequency band, and calculates a power ratio between the firstpower and the second power. The signal analysis unit 273 generates asignal triggering at least one of control of therapy to the patient orrecording of diagnostic information based on the power ratio.

In the example of FIG. 22, chopper-stabilized, superheterodyne amplifier272 modulates the physiological signal with a first carrier frequencyf_(c), amplifies the modulated signal, and demodulates the amplifiedsignal to baseband with a second frequency equivalent to the firstfrequency f_(c) plus (or minus) an offset δ. Signal analysis unit 273measures a characteristic of the demodulated signal in a selectedfrequency band.

The second frequency is different from the first frequency f_(c) and isselected, via the offset 6, to position the demodulated signal in theselected frequency band at the baseband. In particular, the offset maybe selected based on the selected frequency band. For example, thefrequency band may be a frequency within the selected frequency band,such as a center frequency of the band.

If the selected frequency band is 5 to 15 Hz, for example, the offset δmay be the center frequency of this band, i.e., 10 Hz. In some examples,the offset δ may be a frequency elsewhere in the selected frequencyband. However, the center frequency generally will be preferred. Thesecond frequency may be generated by shifting the first frequency by theoffset amount. Alternatively, the second frequency may be generatedindependently of the first frequency such that the difference betweenthe first and second frequencies is the offset.

In either case, the second frequency may be equivalent to the firstfrequency f_(c) plus or minus the offset δ. If the first frequency f_(c)is 4000 Hz, for example, and the selected frequency band is 5 to 15 Hz(the alpha band for EEG signals), the offset δ may be selected as thecenter frequency of that band, i.e., 10 Hz. In this case, the secondfrequency is the first frequency of 4000 Hz plus or minus 10 Hz. Usingthe superheterodyne structure, the signal is modulated at 4000 Hz bymodulator 282, amplified by amplifier 286 and then demodulated bydemodulator 288 at 3990 or 4010 Hz (the first frequency f_(c) of 4000 Hzplus or minus the offset δ of 10 Hz) to position the 5 to 15 Hz bandcentered at 10 Hz at baseband, e.g., DC. In this manner the 5 to 15 Hzband can be directly downconverted such that it is substantiallycentered at DC.

As illustrated in FIG. 22, superheterodyne instrumentation amplifier 272receives a physiological signal (e.g., V_(in)) from sensing elementspositioned at a desired location within a patient or external to apatient to detect the physiological signal. For example, thephysiological signal may comprise one of an EEG, ECoG, EMG, EDG,pressure, temperature, impedance or motion signal. Again, an EEG signalwill be described for purposes of illustration. Superheterodyneinstrumentation amplifier 272 may be configured to receive thephysiological signal (V_(in)) as either a differential or signal-endedinput. Superheterodyne instrumentation amplifier 272 includes firstmodulator 282 for modulating the physiological signal from baseband atthe carrier frequency (f_(c)). In the example of FIG. 22, an inputcapacitance (C_(in)) 283 couples the output of first modulator 282 tofeedback adder 284. Feedback adder 284 will be described below inconjunction with the feedback paths.

Adder 285 represents the inclusion of a noise signal with the modulatedsignal. Adder 285 represents the addition of low frequency noise, butdoes not form an actual component of superheterodyne instrumentationamplifier 272. Adder 285 models the noise that comes intosuperheterodyne instrumentation amplifier 272 from non-ideal transistorcharacteristics. At adder 285, the original baseband components of thesignal are located at the carrier frequency f_(c). As an example, thebaseband components of the signal may have a frequency within a range of0 to approximately 1000 Hz and the carrier frequency f_(c) may beapproximately 4 kHz to approximately 10 kHz. The noise signal enters thesignal pathway, as represented by adder 285, to produce a noisymodulated signal. The noise signal may include 1/f noise, popcorn noise,offset, and any other external signals that may enter the signal pathwayat low (baseband) frequency. At adder 285, however, the originalbaseband components of the signal have already been chopped to a higherfrequency band, e.g., 4000 Hz, by first modulator 282. Thus, thelow-frequency noise signal is segregated from the original basebandcomponents of the signal.

Amplifier 286 receives the noisy modulated input signal from adder 285.Amplifier 286 amplifies the noisy modulated signal and outputs theamplified signal to a second modulator 288. Offset (δ) 287 may be tunedsuch that it is approximately equal to a frequency within the selectedfrequency band, and preferably the center frequency of the selectedfrequency band. The resulting modulation frequency (f_(c)±δ) used bydemodulator 288 is then different from the first carrier frequency f_(c)by the offset amount δ. In some cases, offset δ 287 may be manuallytuned according to the selected frequency band by a physician,technician, or the patient. In other cases, the offset δ 287 may bydynamically tuned to the selected frequency band in accordance withstored frequency band values. For example, different frequency bands maybe scanned by automatically or manually tuning the offset δ according tocenter frequencies of the desired bands. As an example, when monitoringakinesia, the selected frequency band may be the alpha frequency band (5Hz to 15 Hz). In this case, the offset δ may be approximately the centerfrequency of the alpha band, i.e., 10 Hz. As another example, whenmonitoring tremor, the selected frequency band may be the beta frequencyband (15 Hz-35 Hz). In this case, the offset δ may be approximately thecenter frequency of the beta band, i.e., 25 Hz.

As another example, when monitoring intent in the cortex, the selectedfrequency band may be the high gamma frequency band (100 Hz-200 Hz). Inthis case, the offset δ may be approximately the center frequency of thehigh gamma band, i.e., 175 Hz. When monitoring pre-seizure biomarkers inepilepsy, the selected frequency may be fast ripples (500 Hz), in whichcase the offset δ may be approximately 500 Hz. As another illustration,the selected frequency band passed by filter 234 may be the gamma band(30 Hz-80 Hz), in which case the offset δ may be tuned to approximatelythe center frequency of the gamma band, i.e., 55 Hz.

Hence, the signal in the selected frequency band may be produced byselecting the offset (δ) 287 such that the carrier frequency plus orminus the offset frequency (f_(c)±δ) is equal to a frequency within theselected frequency band, such as the center frequency of the selectedfrequency band. In each case, as explained above, the offset may beselected to correspond to the desired band. For example, an offset of 5Hz would place the alpha band at the baseband frequency, e.g., DC, upondownconversion by the demodulator. Similarly, an offset of 15 Hz wouldplace the beta band at DC upon downconversion, and an offset of 30 Hzwould place the gamma band at DC upon downconversion. In this manner,the pertinent frequency band is centered at the baseband. Then, passivelow pass filtering may be applied to select the frequency band. In thismanner, the superheterodyne architecture serves to position the desiredfrequency band at baseband as a function of the selected offsetfrequency used to produce the second frequency for demodulation. Ingeneral, in the example of FIG. 22, powered bandpass filtering is notrequired. Likewise, the selected frequency band can be obtained withoutthe need for oversampling and digitization of the wideband signal.

With further reference to FIG. 22, second modulator 288 demodulates theamplified signal at the second frequency f_(c)±δ, which is separatedfrom the carrier frequency f_(c) by the offset δ. That is, secondmodulator 288 modulates the noise signal up to the f_(c)±δ frequency anddemodulates the components of the signal in the selected frequency banddirectly to baseband. Integrator 289 operates on the demodulated signalto pass the components of the signal in the selected frequency bandpositioned at baseband and substantially eliminate the components of thenoise signal at higher frequencies. In this manner, integrator 289provides compensation and filtering to the amplified signal to producean output signal (V_(out)). In other examples, compensation andfiltering may be provided by other circuitry.

As shown in FIG. 22, superheterodyne instrumentation amplifier 272 mayinclude two negative feedback paths to feedback adder 284 to reduceglitching in the output signal (V_(out)). In particular, the firstfeedback path includes a third modulator 290, which modulates the outputsignal at the carrier frequency plus or minus the offset δ, and afeedback capacitance (C_(fb)) 291 that is selected to produce desiredgain given the value of the input capacitance (C_(in)) 283. The firstfeedback path produces a feedback signal that is added to the originalmodulated signal at feedback adder 284 to produce attenuation andthereby generate gain at the output of amplifier 286.

The second feedback path may be optional, and may include an integrator292, a fourth modulator 293, which modulates the output signal at thecarrier frequency plus or minus the offset δ, and high pass filtercapacitance (C_(hp)) 294. Integrator 292 integrates the output signaland modulator 293 modulates the output of integrator 292 at the carrierfrequency. High pass filter capacitance (C_(hp)) 294 is selected tosubstantially eliminate components of the signal that have a frequencybelow the corner frequency of the high pass filter. For example, thesecond feedback path may set a corner frequency of approximately equalto 2.5 Hz, 0.5 Hz, or 0.05 Hz. The second feedback path produces afeedback signal that is added to the original modulated signal atfeedback adder 284 to increase input impedance at the output ofamplifier 286.

As described above, chopper-stabilized, superheterodyne instrumentationamplifier 272 can be used to achieve direct downconversion of a selectedfrequency band centered at a frequency that is offset from baseband byan amount δ. Again, if the alpha band is centered at 10 Hz, then theoffset amount δ used to produce the demodulation frequency f_(c)±δ maybe 10 Hz. As illustrated in FIG. 22, first modulator 282 is run at thecarrier frequency (f_(c)), which is specified by the 1/f corner andother constraints, while second modulator 288 is run at the selectedfrequency band (f_(c)+δ). Multiplication of the physiological signal bythe carrier frequency convolves the signal in the frequency domain. Thenet effect of upmodulation is to place the signal at the carrierfrequency (f_(c)). By then running second modulator 288 at a differentfrequency (f_(c)±δ), the convolution of the signal sends the signal inthe selected frequency band to baseband and 2δ. Integrator 289 may beprovided to filter out the 2δ component and passes the basebandcomponent of the signal in the selected frequency band.

As illustrated in FIG. 22, signal analysis unit 273 receives the outputsignal from instrumentation amplifier. In the example of FIG. 22, signalanalysis unit 273 includes a passive lowpass filter 274, a powermeasurement module 276, a lowpass filter 277, a threshold tracker 278and a comparator 280. Passive lowpass filter 274 extracts the signal inthe selected frequency band positioned at baseband. For example, lowpassfilter 274 may be configured to reject frequencies above a desiredfrequency, thereby preserving the signal in the selected frequency band.Power measurement module 276 then measures power of the extractedsignal. In some cases, power measurement module 276 may extract the netpower in the desired band by full wave rectification. In other cases,power measurement module 276 may extract the net power in the desiredband by a squaring power calculation, which may be provided by asquaring power circuit. As the signal has sine and cosine phases,summing of the squares yields a net of 1 and the total power. Themeasured power is then filtered by lowpass filter 277 and applied tocomparator 280. Threshold tracker 278 tracks fluctuations in powermeasurements of the selected frequency band over a period of time inorder to generate a baseline power threshold of the selected frequencyband for the patient. Threshold tracker 278 applies the baseline powerthreshold to comparator 280 in response to receiving the measured powerfrom power measurement module 276.

Comparator 280 compares the measured power from lowpass filter 277 withthe baseline power threshold from threshold tracker 278. If the measuredpower is greater than the baseline power threshold, comparator 280 mayoutput a trigger signal to a processor of a medical device to controltherapy and/or recording of diagnostic information. If the measuredpower is equal to or less than the baseline power threshold, comparator280 outputs a power tracking measurement to threshold tracker 278, asindicated by the line from comparator 280 to threshold tracker 278.Threshold tracker 278 may include a median filter that creates thebaseline threshold level after filtering the power of the signal in theselected frequency band for several minutes. In this way, the measuredpower of the signal in the selected frequency band may be used by thethreshold tracker 278 to update and generate the baseline powerthreshold of the selected frequency band for the patient. Hence, thebaseline power threshold may be dynamically adjusted as the sensedsignal changes over time. A signal above or below the baseline powerthreshold may signify an event that may support generation of a triggersignal.

In some cases, frequency selective signal monitor 270 may be limited tomonitoring a single frequency band of the wide band physiological signalat any specific instant. Alternatively, frequency selective signalmonitor 270 may be capable of efficiently hopping frequency bands inorder to monitor the signal in a first frequency band, monitor thesignal in a second frequency band, and then determine whether to triggertherapy and/or diagnostic recording based on some combination of themonitored signals. For example, different frequency bands may bemonitored on an alternating basis to support signal analysis techniquesthat rely on comparison or processing of characteristics associated withmultiple frequency bands.

In some examples, the circuit of FIG. 22 may be modified to furtherincorporate a nested chopper architecture having an outer chopper and aninner chopper. The inner chopper may operate as a superheterodyningchopper (e.g., with modulation and demodulation frequencies of f_(c) andf_(c)±δ, respectively) while the outer chopper may operate as a basicchopper with modulation and demodulation frequencies both at f_(c)/m,where f_(c)/m is lower than f_(c). The addition of an outer chopper toform a nested chopper may be helpful in suppressing intermodulation.

FIG. 23 is a block diagram illustrating a portion of an exemplarychopper-stabilized superheterodyne instrumentation amplifier 272A foruse within frequency selective signal monitor 270 from FIG. 22.Superheterodyne instrumentation amplifier 272A illustrated in FIG. 23may operate substantially similar to superheterodyne instrumentationamplifier 272 from FIG. 22. Superheterodyne instrumentation amplifier272A includes a first modulator 295, an amplifier 297, a frequencyoffset 298, a second modulator 299, and a lowpass filter 300. In someexamples, lowpass filter 300 may be an integrator, such as integrator289 of FIG. 22. Adder 296 represents addition of noise to the choppedsignal. However, adder 296 does not form an actual component ofsuperheterodyne instrumentation amplifier 272A. Adder 296 models thenoise that comes into superheterodyne instrumentation amplifier 272Afrom non-ideal transistor characteristics.

Superheterodyne instrumentation amplifier 272A receives a physiologicalsignal (V_(in)) associated with a patient from sensing elements, such aselectrodes, positioned within or external to the patient to detect thephysiological signal. First modulator 295 modulates the signal frombaseband at the carrier frequency (f_(c)). A noise signal is added tothe modulated signal, as represented by adder 296. Amplifier 297amplifies the noisy modulated signal. Frequency offset 298 is tuned suchthat the carrier frequency plus or minus frequency offset 298 (f_(c)±δ)is equal to the selected frequency band. Hence, the offset δ may beselected to target a desired frequency band. Second modulator 299modulates the noisy amplified signal at offset frequency 98 from thecarrier frequency f_(c). In this way, the amplified signal in theselected frequency band is demodulated directly to baseband and thenoise signal is modulated to the selected frequency band.

Lowpass filter 300 may filter the majority of the modulated noise signalout of the demodulated signal and set the effective bandwidth of itspassband around the center frequency of the selected frequency band. Asillustrated in the detail associated with lowpass filter 300 in FIG. 23,a passband 303 of lowpass filter 300 may be positioned at a centerfrequency of the selected frequency band. In some cases, the offset δmay be equal to this center frequency. Lowpass filter 300 may then setthe effective bandwidth (BW/2) of the passband around the centerfrequency such that the passband encompasses the entire selectedfrequency band. In this way, lowpass filter 300 passes a signal 301positioned anywhere within the selected frequency band. For example, ifthe selected frequency band is 5 to 15 Hz, for example, the offset δ maybe the center frequency of this band, i.e., 10 Hz, and the effectivebandwidth may be half the full bandwidth of the selected frequency band,i.e., 5 Hz. In this case, lowpass filter 300 rejects or at leastattenuates signals above 5 Hz, thereby limiting the passband signal tothe alpha band, which is centered at 0 Hz as a result of thesuperheterodyne process. Hence, the center frequency of the selectedfrequency band can be specified with the offset δ, and the bandwidth BWof the passband can be obtained independently with the lowpass filter300, with BW/2 about each side of the center frequency.

Lowpass filter 300 then outputs a low-noise physiological signal(V_(out)). The low-noise physiological signal may then be input tosignal analysis unit 273 from FIG. 22. As described above, signalanalysis unit 273 may extract the signal in the selected frequency bandpositioned at baseband, measure power of the extracted signal, andcompare the measured power to a baseline power threshold of the selectedfrequency band to determine whether to trigger patient therapy.

FIGS. 24A-24D are graphs illustrating the frequency components of asignal at various stages within superheterodyne instrumentationamplifier 272A of FIG. 23. In particular, FIG. 24A illustrates thefrequency components in a selected frequency band within thephysiological signal received by frequency selective signal monitor 270.The frequency components of the physiological signal are represented byline 302 and located at offset δ from baseband in FIG. 24A.

FIG. 24B illustrates the frequency components of the noisy modulatedsignal produced by modulator 295 and amplifier 297. In FIG. 24B, theoriginal offset frequency components of the physiological signal havebeen up-modulated at carrier frequency f_(c) and are represented bylines 304 at the odd harmonics. The frequency components of the noisesignal added to the modulated signal are represented by dotted line 305.In FIG. 24B, the energy of the frequency components of the noise signalis located substantially at baseband and energy of the frequencycomponents of the desired signal is located at the carrier frequency(f_(c)) plus and minus frequency offset (δ) 298 and its odd harmonics.

FIG. 24C illustrates the frequency components of the demodulated signalproduced by demodulator 299. In particular, the frequency components ofthe demodulated signal are located at baseband and at twice thefrequency offset (2δ), represented by lines 306. The frequencycomponents of the noise signal are modulated and represented by dottedline 307. The frequency components of the noise signal are located atthe carrier frequency plus or minus the offset frequency (δ) 298 and itsodd harmonics in FIG. 24C. FIG. 24C also illustrates the effect oflowpass filter 300 that may be applied to the demodulated signal. Thepassband of lowpass filter 300 is represented by dashed line 308.

FIG. 24D is a graph that illustrates the frequency components of theoutput signal. In FIG. 24D, the frequency components of the outputsignal are represented by line 310 and the frequency components of thenoise signal are represented by dotted line 311. FIG. 24D illustratesthat lowpass filter 300 removes the frequency components of thedemodulated signal located at twice the offset frequency (2δ). In thisway, lowpass filter 300 positions the frequency components of the signalat the desired frequency band within the physiological signal atbaseband. In addition, lowpass filter 300 removes the frequencycomponents from the noise signal that were located outside of thepassband of lowpass filter 300 shown in FIG. 24C. The energy from thenoise signal is substantially eliminated from the output signal, or atleast substantially reduced relative to the original noise signal thatotherwise would be introduced.

FIG. 25 is a block diagram illustrating a portion of an exemplarychopper-stabilized superheterodyne instrumentation amplifier 272B within-phase and quadrature signal paths for use within frequency selectivesignal monitor 270 from FIG. 22. The in-phase and quadrature signalpaths substantially reduce phase sensitivity within superheterodyneinstrumentation amplifier 272B. Because the signal obtained from thepatient and the clocks used to produce the modulation frequencies areuncorrelated, the phase of the signal should be taken into account. Toaddress the phasing issue, two parallel heterodyning amplifiers may bedriven with in-phase (I) and quadrature (Q) clocks created with on-chipdistribution circuits. Net power extraction then can be achieved withsuperposition of the in-phase and quadrature signals.

An analog implementation may use an on-chip self-cascoded Gilbert mixerto calculate the sum of squares. Alternatively, a digital approach maytake advantage of the low bandwidth of the I and Q channels afterlowpass filtering, and digitize at that point in the signal chain fordigital power computation. Digital computation at the I/Q stage hasadvantages. For example, power extraction is more linear than a tan hfunction. In addition, digital computation simplifies offset calibrationto suppress distortion, and preserves the phase information forcross-channel coherence analysis. With either technique, a sum ofsquares in the two channels can eliminate the phase sensitivity betweenthe physiological signal and the modulation clock frequency. The poweroutput signal can lowpass filtered to the order of 1 Hz to track theessential dynamics of a desired biomarker.

Superheterodyne instrumentation amplifier 272B illustrated in FIG. 25may operate substantially similar to superheterodyne instrumentationamplifier 272 from FIG. 22. Superheterodyne instrumentation amplifier272B includes an in-phase (I) signal path with a first modulator 320, anamplifier 322, an in-phase frequency offset (δ) 323, a second modulator324, a lowpass filter 325, and a squaring unit 326. Adder 321 representsaddition of noise. Adder 321 models the noise from non-ideal transistorcharacteristics. Superheterodyne instrumentation amplifier 272B includesa quadrature phase (Q) signal path with a third modulator 328, an adder329, an amplifier 330, a quadrature frequency offset (δ) 331, a fourthmodulator 332, a lowpass filter 333, and a squaring unit 334. Adder 329represents addition of noise. Adder 329 models the noise from non-idealtransistor characteristics.

Superheterodyne instrumentation amplifier 272B receives a physiologicalsignal (V_(in)) associated with a patient from one or more sensingelements. The in-phase (I) signal path modulates the signal frombaseband at the carrier frequency (f_(c)), permits addition of a noisesignal to the modulated signal, and amplifies the noisy modulatedsignal. In-phase frequency offset 323 may be tuned such that it issubstantially equivalent to a center frequency of a selected frequencyband. For the alpha band (5 to 15 Hz), for example, the offset 323 maybe approximately 10 Hz. In this example, if the modulation carrierfrequency f_(c) applied by modulator 320 is 4000 Hz, then thedemodulation frequency f_(c)±δ may be 3990 Hz or 4010 Hz.

Second modulator 324 modulates the noisy amplified signal at a frequency(f_(c)±δ) offset from the carrier frequency f_(c) by the offset amountδ. In this way, the amplified signal in the selected frequency band maybe demodulated directly to baseband and the noise signal may bemodulated up to the second frequency f_(c)±δ. The selected frequencyband of the physiological signal is then substantially centered atbaseband, e.g., DC. For the alpha band (5 to 15 Hz), for example, thecenter frequency of 10 Hz is centered at 0 Hz at baseband. Lowpassfilter 325 filters the majority of the modulated noise signal out of thedemodulated signal and outputs a low-noise physiological signal. Thelow-noise physiological signal may then be squared with squaring unit326 and input to adder 336. In some cases, squaring unit 326 maycomprise a self-cascoded Gilbert mixer. The output of squaring unit 126represents the spectral power of the in-phase signal.

In a similar fashion, the quadrature (Q) signal path modulates thesignal from baseband at the carrier frequency (f_(c)). However, thecarrier frequency applied by modulator 328 in the Q signal path is 90degrees out of phase with the carrier frequency applied by modulator 320in the I signal path. The Q signal path permits addition of a noisesignal to the modulated signal, as represented by adder 329, andamplifies the noisy modulated signal via amplifier 330. Again,quadrature offset frequency (δ) 331 may be tuned such it isapproximately equal to the center frequency of the selected frequencyband. As a result, the demodulation frequency applied to demodulator 332is (f_(c)±δ). In the quadrature signal path, however, an additionalphase shift of 90 degrees is added to the demodulation frequency fordemodulator 332. Hence, the demodulation frequency for demodulator 332,like demodulator 324, is f_(c)±δ. However, the demodulation frequencyfor demodulator 332 is phase shifted by 90 degrees relative to thedemodulation frequency for demodulator 324 of the in-phase signal path.

Fourth modulator 332 modulates the noisy amplified signal at thequadrature frequency 331 from the carrier frequency. In this way, theamplified signal in the selected frequency band is demodulated directlyto baseband and the noise signal is modulated at the demodulationfrequency f_(c)±δ. Lowpass filter 333 filters the majority of themodulated noise signal out of the demodulated signal and outputs alow-noise physiological signal. The low-noise physiological signal maythen be squared and input to adder 336. Like squaring unit 326, squaringunit 334 may comprise a self-cascoded Gilbert mixer. The output ofsquaring unit 334 represents the spectral power of the quadraturesignal.

Adder 336 combines the signals output from squaring unit 326 in thein-phase signal path and squaring unit 334 in the quadrature signalpath. The output of adder 336 may be input to a lowpass filter 337 thatgenerates a low-noise, phase-insensitive output signal (V_(out)). Asdescribed above, the signal may be input to signal analysis unit 273from FIG. 22. As described above, signal analysis unit 273 may extractthe signal in the selected frequency band positioned at baseband,measure power of the extracted signal, and compare the measured power toa baseline power threshold of the selected frequency band to determinewhether to trigger patient therapy. Alternatively, signal analysis unit273 may analyze other characteristics of the signal. The signal Vout maybe applied to the signal analysis unit 273 as an analog signal.Alternatively, an analog-to-digital converter (ADC) may be provided toconvert the signal Vout to a digital signal for application to signalanalysis unit 273. Hence, signal analysis unit 273 may include one ormore analog components, one or more digital components, or a combinationof analog and digital components.

FIG. 26 is a circuit diagram illustrating an example mixer amplifiercircuit 400 for use in superheterodyne instrumentation amplifier 272 ofFIG. 22. For example, circuit 400 represents an example of amplifier286, demodulator 288 and integrator 289 in FIG. 22. Although the exampleof FIG. 26 illustrates a differential input, circuit 400 may beconstructed with a single-ended input. Accordingly, circuit 400 of FIG.26 is provided for purposes of illustration, without limitation as toother examples. In FIG. 26, VDD and VSS indicate power and groundpotentials, respectively.

Mixer amplifier circuit 400 amplifies a noisy modulated input signal toproduce an amplified signal and demodulates the amplified signal. Mixeramplifier circuit 400 also substantially eliminates noise from thedemodulated signal to generate the output signal. In the example of FIG.26, mixer amplifier circuit 400 is a modified folded-cascode amplifierwith switching at low impedance nodes. The modified folded-cascodearchitecture allows currents to be partitioned to maximize noiseefficiency. In general, the folded cascode architecture is modified inFIG. 26 by adding two sets of switches. One set of switches isillustrated in FIG. 26 as switches 402A and 402B (collectively referredto as “switches 402”) and the other set of switches includes switches404A and 404B (collectively referred to as “switches 404”).

Switches 402 are driven by chop logic to support the chopping of theamplified signal for demodulation at the chop frequency. In particular,switches 402 demodulate the amplified signal and modulate front-endoffsets and 1/f noise. Switches 404 are embedded within a self-biasedcascode mirror formed by transistors M6, M7, M8 and M9, and are drivenby chop logic to up-modulate the low frequency errors from transistorsM8 and M9. Low frequency errors in transistors M6 and M7 are attenuatedby source degeneration from transistors M8 and M9. The output of mixeramplifier circuit 400 is at baseband, allowing an integrator formed bytransistor M10 and capacitor 406 (Ccomp) to stabilize a feedback path(not shown in FIG. 26) between the output and input and filter modulatedoffsets.

In the example of FIG. 26, mixer amplifier circuit 400 has three mainblocks: a transconductor, a demodulator, and an integrator. The core issimilar to a folded cascode. In the transconductor section, transistorM5 is a current source for the differential pair of input transistors M1and M2. In some examples, transistor M5 may pass approximately 800 nA,which is split between transistors M1 and M2, e.g., 400 nA each.Transistors M1 and M2 are the inputs to amplifier 286. Small voltagedifferences steer differential current into the drains of transistors M1and M2 in a typical differential pair way. Transistors M3 and M4 serveas low side current sinks, and may each sink roughly 500 nA, which is afixed, generally nonvarying current. Transistors M1, M2, M3, M4 and M5together form a differential transconductor.

In this example, approximately 100 nA of current is pulled through eachleg of the demodulator section. The AC current at the chop frequencyfrom transistors M1 and M2 also flows through the legs of thedemodulator. Switches 402 alternate the current back and forth betweenthe legs of the demodulator to demodulate the measurement signal back tobaseband, while the offsets from the transconductor are up-modulated tothe chopper frequency. As discussed previously, transistors M6, M7, M8and M9 form a self-biased cascode mirror, and make the signalsingle-ended before passing into the output integrator formed bytransistor M10 and capacitor 406 (Ccomp). Switches 404 placed within thecascode (M6-M9) upmodulate the low frequency errors from transistors M8and M9, while the low frequency errors of transistor M6 and transistorM7 are suppressed by the source degeneration they see from transistorsM8 and M9. Source degeneration also keeps errors from Bias N2transistors 408 suppressed. Bias N2 transistors M12 and M13 form acommon gate amplifier that presents a low impedance to the chopperswitching and passes the signal current to transistors M6 and M7 withimmunity to the voltage on the drains.

The output DC signal current and the upmodulated error current pass tothe integrator, which is formed by transistor M10, capacitor 406, andthe bottom NFET current source transistor M11. Again, this integratorserves to both stabilize the feedback path and filter out theupmodulated error sources. The bias for transistor M10 may beapproximately 100 nA, and is scaled compared to transistor M8. The biasfor lowside NFET M11 may also be approximately 100 nA (sink). As aresult, the integrator is balanced with no signal. If more current driveis desired, current in the integration tail can be increasedappropriately using standard integrate circuit design techniques.Various transistors in the example of FIG. 26 may be field effecttransistors (FETs), and more particularly CMOS transistors.

FIG. 27 is a circuit diagram illustrating an instrumentation amplifier410 with differential inputs V_(in)+ and V_(in)−. Instrumentationamplifier 410 is an example superheterodyne instrumentation amplifier272 previously described in this disclosure with reference to FIG. 22.FIG. 27 uses several reference numerals from FIG. 22 to refer to likecomponents. However, the optional high pass filter feedback pathcomprising components 292, 293 and 294 is omitted from the example ofFIG. 27. In general, instrumentation amplifier 410 may be constructed asa single-ended or differential amplifier. The example of FIG. 27illustrates example circuitry for implementing a differential amplifier.The circuitry of FIG. 27 may be configured for use in each of the I andQ signal paths of FIG. 25.

In the example of FIG. 27, instrumentation amplifier 410 includes aninterface to one or more sensing elements that produce a differentialinput signal providing voltage signals V_(in)+, V_(in)−. Thedifferential input signal may be provided by a sensor comprising any ofa variety of sensing elements, such as a set of one or more electrodes,an accelerometer, a pressure sensor, a force sensor, a gyroscope, ahumidity sensor, a chemical sensor, or the like. For brain sensing, thedifferential signal V_(in)+, V_(in)− may be, for example, an EEG or EcoGsignal.

The differential input voltage signals are connected to respectivecapacitors 283A and 283B (collectively referred to as “capacitors 283”)through switches 412A and 412B, respectively. Switches 412A and 412B maycollectively form modulator 282 of FIG. 22. Switches 412A, 412B aredriven by a clock signal provided by a system clock (not shown) at thecarrier frequency f_(c). Switches 412A, 412B may be cross-coupled toeach other, as shown in FIG. 27, to reject common-mode signals.Capacitors 283 are coupled at one end to a corresponding one of switches412A, 412B and to a corresponding input of amplifier 286 at the otherend. In particular, capacitor 283A is coupled to the positive input ofamplifier 286, and capacitor 283B is coupled to the negative input ofamplifier 286, providing a differential input. Amplifier 286, modulator288 and integrator 289 together may form a mixer amplifier, which may beconstructed similar to mixer amplifier 400 of FIG. 26.

In FIG. 27, switches 412A, 412B and capacitors 283A, 283B form a frontend of instrumentation amplifier 410. In particular, the front end mayoperate as a continuous time switched capacitor network. Switches 412A,412B toggle between an open state and a closed state in which inputssignals V_(in)+, V_(in)− are coupled to capacitors 283A, 283B at a clockfrequency f_(c) to modulate (chop) the input signal to the carrier(clock) frequency. As mentioned previously, the input signal may be alow frequency signal within a range of approximately 0 Hz toapproximately 1000 Hz and, more particularly, approximately 0 Hz to 500Hz, and still more particularly less than or equal to approximately 100Hz. The carrier frequency may be within a range of approximately 4 kHzto approximately 10 kHz. Hence, the low frequency signal is chopped tothe higher chop frequency band.

Switches 412A, 412B toggle in-phase with one another to provide adifferential input signal to amplifier 286. During one phase of theclock signal f_(c), switch 412A connects Vin+ to capacitor 283A andswitch 412B connects Vin− to capacitor 283B. During another phase,switches 412A, 412B change state such that switch 412A decouples Vin+from capacitor 283A and switch 412B decouples Vin− from capacitor 283B.Switches 412A, 412B synchronously alternate between the first and secondphases to modulate the differential voltage at the carrier frequency.The resulting chopped differential signal is applied across capacitors283A, 283B, which couple the differential signal across the positive andnegative inputs of amplifier 286.

Resistors 414A and 414B (collectively referred to as “resistors 414”)may be included to provide a DC conduction path that controls thevoltage bias at the input of amplifier 286. In other words, resistors414 may be selected to provide an equivalent resistance that is used tokeep the bias impedance high. Resistors 414 may, for example, beselected to provide a 5 GΩ equivalent resistor, but the absolute size ofthe equivalent resistor is not critical to the performance ofinstrumentation amplifier 410. In general, increasing the impedanceimproves the noise performance and rejection of harmonics, but extendsthe recovery time from an overload. To provide a frame of reference, a 5GΩ equivalent resistor results in a referred-to-input (RTI) noise ofapproximately 20 nV/rt Hz with an input capacitance (Cin) ofapproximately 25 pF. In light of this, a stronger motivation for keepingthe impedance high is the rejection of high frequency harmonics whichcan alias into the signal chain due to settling at the input nodes ofamplifier 286 during each half of a clock cycle.

Resistors 414 are merely exemplary and serve to illustrate one of manydifferent biasing schemes for controlling the signal input to amplifier286. In fact, the biasing scheme is flexible because the absolute valueof the resulting equivalent resistance is not critical. In general, thetime constant of resistor 414 and input capacitor 283 may be selected tobe approximately 100 times longer than the reciprocal of the choppingfrequency.

Amplifier 286 may produce noise and offset in the differential signalapplied to its inputs. For this reason, the differential input signal ischopped via switches 412A, 412B and capacitors 283A, 283B to place thesignal of interest in a different frequency band from the noise andoffset. Then, instrumentation amplifier 410 chops the amplified signalat modulator 88 a second time to demodulate the signal of interest downto baseband while modulating the noise and offset up to the chopfrequency band. In this manner, instrumentation amplifier 410 maintainssubstantial separation between the noise and offset and the signal ofinterest.

Modulator 288 may support direct downconversion of the selectedfrequency band using a superheterodyne process. In particular, modulator288 may demodulate the output of amplifier 86 at a frequency equal tothe carrier frequency f_(c) used by switches 412A, 412B plus or minus anoffset δ that is substantially equal to the center frequency of theselected frequency band. In other words, modulator 88 demodulates theamplified signal at a frequency of f_(c)±δ. Integrator 289 may beprovided to integrate the output of modulator 288 to produce outputsignal Vout. Amplifier 286 and differential feedback path branches 416A,416B process the noisy modulated input signal to achieve a stablemeasurement of the low frequency input signal output while operating atlow power.

Operating at low power tends to limit the bandwidth of amplifier 286 andcreates distortion (ripple) in the output signal. Amplifier 286,modulator 288, integrator 289 and feedback paths 416A, 416B maysubstantially eliminate dynamic limitations of chopper stabilizationthrough a combination of chopping at low-impedance nodes and ACfeedback, respectively.

In FIG. 27, amplifier 286, modulator 288 and integrator 289 arerepresented with appropriate circuit symbols in the interest ofsimplicity. However, it should be understood that such components may beimplemented in accordance with the circuit diagram of mixer amplifiercircuit 400 provided in FIG. 26. Instrumentation amplifier 410 mayprovide synchronous demodulation with respect to the input signal andsubstantially eliminate 1/f noise, popcorn noise, and offset from thesignal to output a signal that is an amplified representation of thedifferential voltage Vin+, Vin−.

Without the negative feedback provided by feedback path 416A, 416B, theoutput of amplifier 286, modulator 288 and integrator 289 could includespikes superimposed on the desired signal because of the limitedbandwidth of the amplifier at low power. However, the negative feedbackprovided by feedback path 416A, 416B suppresses these spikes so that theoutput of instrumentation amplifier 410 in steady state is an amplifiedrepresentation of the differential voltage produced across the inputs ofamplifier 286 with very little noise.

Feedback paths 416A, 216B, as shown in FIG. 27, include two feedbackpath branches that provide a differential-to-single ended interface.Amplifier 286, modulator 288 and integrator 289 may be referred tocollectively as a mixer amplifier. The top feedback path branch 416Amodulates the output of this mixer amplifier to provide negativefeedback to the positive input terminal of amplifier 286. The topfeedback path branch 416A includes capacitor 418A and switch 420A.Similarly, the bottom feedback path branch 416B includes capacitor 418Band switch 420B that modulate the output of the mixer amplifier toprovide negative feedback to the negative input terminal of the mixeramplifier. Capacitors 418A, 418B are connected at one end to switches420A, 420B, respectively, and at the other end to the positive andnegative input terminals of the mixer amplifier, respectively.Capacitors 418A, 418B may correspond to capacitor 291 in FIG. 22.Likewise, switches 420A, 420B may correspond to modulator 290 of FIG.22.

Switches 420A and 420B toggle between a reference voltage (Vref) and theoutput of the mixer amplifier 400 to place a charge on capacitors 418Aand 418B, respectively. The reference voltage may be, for example, amid-rail voltage between a maximum rail voltage of amplifier 286 andground. For example, if the amplifier circuit is powered with a sourceof 0 to 2 volts, then the mid-rail Vref voltage may be on the order of 1volt. Switches 420A and 420B should be 180 degrees out of phase witheach other to ensure that a negative feedback path exists during eachhalf of the clock cycle. One of switches 420A, 420B should also besynchronized with the mixer amplifier 400 so that the negative feedbacksuppresses the amplitude of the input signal to the mixer amplifier tokeep the signal change small in steady state. Hence, a first one of theswitches 420A, 420B may modulate at a frequency of f_(c)±δ, while asecond switch 420A, 420B modulates at a frequency of f_(c)±δ, but 180degrees out of phase with the first switch. By keeping the signal changesmall and switching at low impedance nodes of the mixer amplifier, e.g.,as shown in the circuit diagram of FIG. 26, the only significant voltagetransitions occur at switching nodes. Consequently, glitching (ripples)is substantially eliminated or reduced at the output of the mixeramplifier.

Switches 412 and 420, as well as the switches at low impedance nodes ofthe mixer amplifier, may be CMOS SPDT switches. CMOS switches providefast switching dynamics that enables switching to be viewed as acontinuous process. The transfer function of instrumentation amplifier210 may be defined by the transfer function provided in equation (1)below, where Vout is the voltage of the output of mixer amplifier 400,Cin is the capacitance of input capacitors 283, ΔVin is the differentialvoltage at the inputs to amplifier 286, Cfb is the capacitance offeedback capacitors 418A, 418B, and Vref is the reference voltage thatswitches 420A, 420B mix with the output of mixer amplifier 400.Vout=Cin(ΔVin)/Cfb+Vref  (1)From equation (1), it is clear that the gain of instrumentationamplifier 410 is set by the ratio of input capacitors Cin and feedbackcapacitors Cfb, i.e., capacitors 283 and capacitors 418. The ratio ofCin/Cfb may be selected to be on the order of 100. Capacitors 418 may bepoly-poly, on-chip capacitors or other types of MOS capacitors andshould be well matched, i.e., symmetrical.

Although not shown in FIG. 27, instrumentation amplifier 410 may includeshunt feedback paths for auto-zeroing amplifier 410. The shunt feedbackpaths may be used to quickly reset amplifier 410. An emergency rechargeswitch also may be provided to shunt the biasing node to help reset theamplifier quickly. The function of input capacitors 283 is toup-modulate the low-frequency differential voltage and rejectcommon-mode signals. As discussed above, to achieve up-modulation, thedifferential inputs are connected to sensing capacitors 283A, 283Bthrough SPDT switches 412A, 412B, respectively. The phasing of theswitches provides for a differential input to amplifier 286. Theseswitches 412A, 412B operate at the clock frequency, e.g., 4 kHz. Becausecapacitors 283A, 283B toggle between the two inputs, the differentialvoltage is up-modulated to the carrier frequency while the low-frequencycommon-mode signals are suppressed by a zero in the charge transferfunction. The rejection of higher-bandwidth common signals relies onthis differential architecture and good matching of the capacitors.

Blanking circuitry may be provided in some examples for applications inwhich measurements are taken in conjunction with stimulation pulsesdelivered by a cardiac pacemaker, cardiac defibrillator, orneurostimulator. Such blanking circuitry may be added between the inputsof amplifier 286 and coupling capacitors 283A, 283B to ensure that theinput signal settles before reconnecting amplifier 86 to the inputsignal. For example, the blanking circuitry may be a blankingmultiplexer (MUX) that selectively couples and decouples amplifier 286from the input signal. This blanking circuitry may selectively decouplethe amplifier 286 from the differential input signal and selectivelydisable the first and second modulators, i.e., switches 412, 420, e.g.,during delivery of a stimulation pulse.

A blanking MUX is optional but may be desirable. The clocks drivingswitches 412, 420 to function as modulators cannot be simply shut offbecause the residual offset voltage on the mixer amplifier wouldsaturate the amplifier in a few milliseconds. For this reason, ablanking MUX may be provided to decouple amplifier 86 from the inputsignal for a specified period of time during and following applicationof stimulation by a cardiac pacemaker or defibrillator, or by aneurostimulator.

To achieve suitable blanking, the input and feedback switches 412, 420should be disabled while the mixer amplifier continues to demodulate theinput signal. This holds the state of integrator 289 within the mixeramplifier because the modulated signal is not present at the inputs ofthe integrator, while the demodulator continues to chop the DC offsets.Accordingly, a blanking MUX may further include circuitry or beassociated with circuitry configured to selectively disable switches412, 420 during a blanking interval. Post blanking, the mixer amplifiermay require additional time to resettle because some perturbations mayremain. Thus, the total blanking time includes time for demodulating theinput signal while the input switches 412, 420 are disabled and time forsettling of any remaining perturbations. An example blanking timefollowing application of a stimulation pulse may be approximately 8 mswith 5 ms for the mixer amplifier and 3 ms for the AC couplingcomponents.

Examples of various additional chopper amplifier circuits that may besuitable for or adapted to the techniques, circuits and devices of thisdisclosure are described in U.S. patent application Ser. No. 11/700,404,filed Jan. 31, 2007, by Timothy J. Denison, now U.S. Pat. No. 7,385,443to Denison, entitled “Chopper Stabilized Instrumentation Amplifier,” theentire content of which is incorporated herein by reference.

Various examples of systems and techniques for controlling a therapydelivery device have been described. These and other examples are withinthe scope of the following claims. While some therapy systems andmethods have primarily been described with reference to determiningwhether patient 12 is in a movement state based on EEG signals, in otherexamples, other neural based bioelectrical signals may be useful. Otheruseful neural-based bioelectrical signals include electrical signalsfrom regions of brain 20 deeper than the signals reflected in the EEG,such as an ECoG signal that measures electrical signals on a surface ofbrain 20. As other examples of bioelectrical signals of brain 20 thatmay be used to detect a movement state of patient 12, electrodes placedwithin the motor cortex or other regions of brain 20 may detect fieldpotentials within the particular region of the brain, and the fieldpotential may be indicative of a movement state. The particularbioelectrical signal that is indicative of the movement state may bedetermined during a trial stage, as described above with respect to theEEG signal.

In addition, a processor may employ any suitable signal processingtechnique to determine whether the bioelectrical signal indicates themovement state. For example, as described above with respect to EEGsignals, an ECoG or field potential signal may be analyzed for arelationship between a voltage or amplitude of the signal and athreshold value, temporal correlation or frequency correlation with atemplate signal, power levels within one or more frequency bands, ratiosof power levels within two or more frequency bands, or combinationsthereof.

While the above techniques for analyzing a brain signal within the DLPFcortex 132 (FIG. 15) were described primarily with reference to IMD 134(FIG. 15), in other examples, a processor within another device,implanted or external, may determine whether a brain signal within theDLPF cortex 132 indicates prospective movement. In addition, whilesignal processing is described primarily with reference to processor 72of IMD 134, in other examples, the signal processor for processing theelectrical activity sensed within DLPF cortex 132 may be integrated withsensing module 136 of IMD 134, external sensing device 14 (FIG. 1A) orany other suitable device.

The invention claimed is:
 1. A method comprising: sensing a brain signalwithin a dorsal-lateral prefrontal cortex of a brain of a patient;determining the brain signal indicates prospective movement of thepatient; and controlling operation of a device, based on determining thebrain signal indicates prospective movement of the patient, to deliverat least one neurostimulation therapy or fluid therapy to the patient.2. The method of claim 1, wherein controlling operation of the devicebased on determining the brain signal indicates prospective movementcomprises controlling operation of the device to cause movement of thepatient.
 3. The method of claim 1, wherein the device comprises a firstdevice, the method further comprising controlling a second device tocause movement of the patient, wherein the second device includes one ofa patient transport device or a patient limb movement device.
 4. Themethod of claim 1, wherein the device comprises a first device, themethod further comprising controlling operation of a second device basedon determining the brain signal indicates prospective movement of thepatient, wherein the second device includes a nonmedical appliance. 5.The method of claim 1, wherein controlling operation of the device basedon determining the brain signal indicates prospective movement of thepatient comprises controlling delivery of movement disorder therapy tothe patient based on determining the brain signal indicates prospectivemovement, the movement disorder therapy comprising the at least one ofneurostimulation therapy or fluid therapy.
 6. The method of claim 5,wherein controlling delivery of movement disorder therapy comprisesinitiating delivery of the movement disorder therapy based ondetermining the brain signal indicates prospective movement.
 7. Themethod of claim 5, wherein controlling delivery of movement disordertherapy comprises adjusting delivery of the movement disorder therapybased on determining the brain signal indicates prospective movement. 8.The method of claim 5, further comprising controlling the device to stopdelivery of movement disorder therapy after a predetermined period oftime.
 9. The method of claim 5, wherein the brain signal comprises afirst brain signal, the method further comprising: sensing a secondbrain signal indicative of lack of prospective movement of the patientwithin the dorsal-lateral prefrontal cortex of the brain of the patient;determining the second brain signal indicates a lack of prospectivemovement of the patient; and controlling the device to stop delivery ofmovement disorder therapy based on determining the second brain signalindicates the lack of prospective movement of the patient.
 10. Themethod of claim 1, wherein the device comprises a first device, themethod further comprising controlling operation of a second device,based on determining the brain signal indicates prospective movement ofthe patient, to deliver functional electrical stimulation therapy to thepatient.
 11. The method of claim 1, further comprising controllingdelivery of an external cue to the patient based on determining thebrain signal indicates prospective movement.
 12. The method of claim 1,further comprising controlling delivery of a sensory cue to the patientbased on determining the brain signal indicates prospective movement.13. The method of claim 1, wherein determining the brain signalindicates prospective movement of the patient comprises determining thebrain signal indicates prospective movement based on one or morefrequency characteristics of the sensed brain signal.
 14. The method ofclaim 1, wherein determining the brain signal indicates prospectivemovement of the patient comprises determining the brain signal indicatesprospective movement based on at least one of a comparison of anamplitude of the brain signal to a threshold value, or temporalcorrelation or frequency correlation of the brain signal with a templatesignal.
 15. The method of claim 1, wherein determining the brain signalindicates prospective movement of the patient comprises at least one ofcomparing a slope of the brain signal over time to slope trendinformation or comparing a timing between inflection points of the brainsignal over time to inflection point trend information.
 16. The methodof claim 1, wherein determining the brain signal indicates prospectivemovement of the patient comprises detecting a biomarker in the sensedbrain signal, the biomarker being associated with prospective movementof the patient.
 17. The method of claim 1, wherein controlling operationof the device comprises controlling operation of the device to deliverneurostimulation therapy to the patient, the neurostimulation therapycomprising deep brain stimulation therapy.
 18. A system comprising: asensing module configured to sense a brain signal within adorsal-lateral prefrontal cortex of a brain of the patient; and acontroller configured to determine the brain signal indicatesprospective movement of the patient and control a device, based on thedetermination the brain signal indicates prospective movement of thepatient, to deliver at least one of neurostimulation therapy or fluidtherapy to the patient.
 19. The system of claim 18, further comprisingthe device, wherein the controller is configured to control the deviceto cause movement of the patient based on the determination the brainsignal indicates prospective movement of the patient.
 20. The system ofclaim 18, further comprising at least one of a patient transport deviceor a patient limb movement device, wherein the controller is configuredto control the at least one of the patient transport device or thepatient limb movement device based on the determination the brain signalindicates prospective movement of the patient.
 21. The system of claim18, wherein the controller is configured to control the device todeliver movement disorder therapy to the patient based on thedetermination the brain signal indicates prospective movement of thepatient.
 22. The system of claim 21, further comprising an electricalstimulator, wherein the device comprises the electrical stimulator, andwherein the movement disorder therapy comprises electrical stimulationtherapy delivered to the patient via the electrical stimulator.
 23. Thesystem of claim 21, wherein the device comprises a first device, thesystem further comprising: the first device, the first device comprisingone of a neurostimulator or a fluid delivery device; and a seconddevice, the second device being configured to deliver a sensory cue tothe patient, wherein the movement disorder therapy comprises the sensorycue.
 24. The system of claim 21, wherein the controller is configured tocontrol the device to stop delivery of the movement disorder therapyafter a predetermined period of time.
 25. The system of claim 21,wherein the brain signal comprises a first brain signal, and the sensingmodule is configured to sense a second brain signal within thedorsal-lateral prefrontal cortex of the brain of the patient, andwherein the controller is configured to determine the second brainsignal indicates a lack of prospective movement of the patient, andcontrol the device to stop delivery of movement disorder therapy basedon the determination that the second brain signal indicates the lack ofprospective movement of the patient.
 26. The system of claim 18, furthercomprising a fluid delivery device, wherein the device comprises thefluid delivery device, and wherein the movement disorder therapycomprises the fluid therapy delivered to the patient via the fluiddelivery device.
 27. The system of claim 18, wherein the controller isfurther configured to determine the brain signal indicates prospectivemovement based on one or more frequency characteristics of the brainsignal.
 28. The system of claim 18, wherein the controller is furtherconfigured to determine the brain signal indicates prospective movementbased on at least one of a comparison of an amplitude of the brainsignal to a threshold value, or temporal correlation or frequencycorrelation of the brain signal with a template signal.
 29. The systemof claim 18, wherein the controller is further configured to determinethe brain signal indicates prospective movement based on comparing aslope of the brain signal over time or based on comparing a timingbetween inflection points of the brain signal over time to trendinformation.
 30. The system of claim 18, wherein the controller isfurther configured to determine the brain signal indicates prospectivemovement by at least detecting a biomarker indicative of prospectivemovement of the patient in the brain signal.
 31. The system of claim 18,further comprising the device, wherein the controller is configured tocontrol the device to deliver neurostimulation therapy to the patientbased on the determination the brain signal indicates prospectivemovement of the patient, the neurostimulation therapy comprising deepbrain stimulation therapy.
 32. A system comprising: means for sensing abrain signal within a dorsal-lateral prefrontal cortex of a brain of thepatient; means for determining the brain signal indicates prospectivemovement of the patient; and means for controlling operation of adevice, based on determining the brain signal indicates prospectivemovement of the patient, to deliver at least one of neurostimulationtherapy or fluid therapy to the patient.
 33. The system of claim 32,further comprising the device, wherein the device comprises means fordelivering movement disorder therapy to the patient.
 34. The system ofclaim 32, wherein the means for controlling operation of the device isconfigured to control the device to deliver neurostimulation therapy tothe patient based on determining the brain signal indicates prospectivemovement of the patient, the neurostimulation therapy comprising deepbrain stimulation therapy.
 35. A non-transitory computer-readable mediumcomprising instructions that, when executed by the processor, cause theprocessor to: receive a brain signal sensed within a dorsal-lateralprefrontal cortex of a brain of the patient; determine the brain signalindicates prospective movement of the patient; and control operation ofa device, based on determining the brain signal indicates prospectivemovement of the patient, to deliver at least one of neurostimulationtherapy or fluid therapy to the patient.